Cheng, K.S.[Kuo-Sheng],
Lin, J.S.[Jzau-Sheng],
Mao, C.W.[Chi-Wu],
The Application of Competitive Hopfield Neural Network to Medical Image
Segmentation,
MedImg(15), No. 4, August 1996, pp. 560-567.
IEEE Top Reference.
0203
BibRef
Hansen, M.W.[Michael W.],
Higgins, W.E.[William E.],
Relaxation Methods for Supervised Image Segmentation,
PAMI(19), No. 9, September 1997, pp. 949-962.
IEEE DOI
9710
BibRef
Earlier:
Watershed-driven relaxation labeling for image segmentation,
ICIP94(III: 460-464).
IEEE DOI
9411
Watershed driven relaxation labeling. Applied to 3D medical images.
Use cues that indicate region shape.
BibRef
Gal, Y.[Yaniv],
Mehnert, A.[Andrew],
Rose, S.[Stephen],
Crozier, S.[Stuart],
Mutual information-based binarisation of multiple images of an object:
An application in medical imaging,
IET-CV(7), No. 3, 2013, pp. 163-169.
DOI Link
1307
BibRef
Cardoso, M.J.[M. Jorge],
Modat, M.[Marc],
Wolz, R.,
Melbourne, A.,
Cash, D.[David],
Rueckert, D.,
Ourselin, S.[Sebastien],
Geodesic Information Flows: Spatially-Variant Graphs and Their
Application to Segmentation and Fusion,
MedImg(34), No. 9, September 2015, pp. 1976-1988.
IEEE DOI
1509
Image segmentation
Clinical annotations.
BibRef
Korner, M.[Marco],
Krishna, M.V.[Mahesh V.],
Susse, H.[Herbert],
Ortmann, W.[Wolfang],
Denzler, J.[Joachim],
Regularized Geometric Hulls for Bio-medical Image Segmentation,
BMVA(2015), No. 4, 2015, pp. 1-12.
PDF File.
1509
BibRef
Bi, L.[Lei],
Feng, D.D.[David Dagan],
Kim, J.M.[Jin-Man],
Dual-Path Adversarial Learning for Fully Convolutional Network
(FCN)-Based Medical Image Segmentation,
VC(34), No. 6-8, June 2018, pp. 1043-1052.
WWW Link.
1806
BibRef
Schipaanboord, B.,
Boukerroui, D.,
Peressutti, D.,
van Soest, J.,
Lustberg, T.,
Kadir, T.,
Dekker, A.,
van Elmpt, W.,
Gooding, M.,
Can Atlas-Based Auto-Segmentation Ever Be Perfect? Insights From
Extreme Value Theory,
MedImg(38), No. 1, January 2019, pp. 99-106.
IEEE DOI
1901
Image segmentation, Databases, Computed tomography, Planning, Head,
Neck, Tumors, Radiotherapy, extreme value theory,
auto-contouring
BibRef
Lu, L.,
Harrison, A.P.,
Deep Medical Image Computing in Preventive and Precision Medicine,
MultMedMag(25), No. 3, July 2018, pp. 109-113.
IEEE DOI
1901
Biomedical imaging, Tumors, Computed tomography,
Image segmentation, Biomarkers
BibRef
Karimi, D.,
Salcudean, S.E.,
Reducing the Hausdorff Distance in Medical Image Segmentation With
Convolutional Neural Networks,
MedImg(39), No. 2, February 2020, pp. 499-513.
IEEE DOI
2002
Image segmentation, Biomedical imaging, Training,
Sensitivity, convolutional neural networks
BibRef
Lin, D.Y.[Dong-Yun],
Li, Y.Q.[Yi-Qun],
Nwe, T.L.[Tin Lay],
Dong, S.[Sheng],
Oo, Z.M.[Zaw Min],
RefineU-Net: Improved U-Net with progressive global feedbacks and
residual attention guided local refinement for medical image
segmentation,
PRL(138), 2020, pp. 267-275.
Elsevier DOI
2010
U-Net, Medical image segmentation,
Progressive global feedbacks, Local refinement, Residual attention gate
BibRef
Mehrtash, A.,
Wells, W.M.,
Tempany, C.M.,
Abolmaesumi, P.,
Kapur, T.,
Confidence Calibration and Predictive Uncertainty Estimation for Deep
Medical Image Segmentation,
MedImg(39), No. 12, December 2020, pp. 3868-3878.
IEEE DOI
2012
Uncertainty, Image segmentation, Calibration, Estimation,
Biomedical imaging, Artificial neural networks, Bayes methods,
fully convolutional neural networks
BibRef
Eelbode, T.,
Bertels, J.,
Berman, M.,
Vandermeulen, D.,
Maes, F.,
Bisschops, R.,
Blaschko, M.B.,
Optimization for Medical Image Segmentation:
Theory and Practice When Evaluating With Dice Score or Jaccard Index,
MedImg(39), No. 11, November 2020, pp. 3679-3690.
IEEE DOI
2011
Indexes, Image segmentation, Biomedical imaging, Measurement,
Risk management, Training, Task analysis, Dice, Jaccard,
Tversky
BibRef
Ren, X.,
Ahmad, S.,
Zhang, L.,
Xiang, L.,
Nie, D.,
Yang, F.,
Wang, Q.,
Shen, D.,
Task Decomposition and Synchronization for Semantic Biomedical Image
Segmentation,
IP(29), 2020, pp. 7497-7510.
IEEE DOI
2007
Semantic segmentation, fully convolutional network,
task decomposition, sync-regularization, deep learning
BibRef
Wang, D.[Dan],
Hu, G.Q.[Guo-Qing],
Lyu, C.Z.[Cheng-Zhi],
Multi-path connected network for medical image segmentation,
JVCIR(71), 2020, pp. 102852.
Elsevier DOI
2009
Medical image segmentation, Multi-path connections,
Convolutional neural networks, Encoder-decoder structure
BibRef
Kim, B.N.[Bach Ngoc],
Dolz, J.[Jose],
Jodoin, P.M.[Pierre-Marc],
Desrosiers, C.[Christian],
Privacy-Net: An Adversarial Approach for Identity-Obfuscated
Segmentation of Medical Images,
MedImg(40), No. 7, July 2021, pp. 1737-1749.
IEEE DOI
2107
Image segmentation, Task analysis, Biomedical imaging, Servers,
Training, Privacy, Image analysis, Adversarial, deep learning,
segmentation
BibRef
Huang, H.M.[Hui-Min],
Zheng, H.[Han],
Lin, L.F.[Lan-Fen],
Cai, M.[Ming],
Hu, H.J.[Hong-Jie],
Zhang, Q.W.[Qiao-Wei],
Chen, Q.Q.[Qing-Qing],
Iwamoto, Y.[Yutaro],
Han, X.H.[Xian-Hua],
Chen, Y.W.[Yen-Wei],
Tong, R.F.[Ruo-Feng],
Medical Image Segmentation With Deep Atlas Prior,
MedImg(40), No. 12, December 2021, pp. 3519-3530.
IEEE DOI
2112
Image segmentation, Bayes methods, Probabilistic logic,
Deep learning, Adaptation models, Task analysis,
adaptive bayesian loss
BibRef
Yu, Q.[Qian],
Gao, Y.[Yang],
Zheng, Y.F.[Ye-Feng],
Zhu, J.B.[Jian-Bing],
Dai, Y.K.[Ya-Kang],
Shi, Y.H.[Ying-Huan],
Crossover-Net: Leveraging vertical-horizontal crossover relation for
robust medical image segmentation,
PR(113), 2021, pp. 107756.
Elsevier DOI
2103
Code, Segmentation.
WWW Link. Convolutional neural network, Non-elongated tissue,
Crossover-Net, Image segmentation, Crossover-patch
BibRef
Nath, V.[Vishwesh],
Yang, D.[Dong],
Landman, B.A.[Bennett A.],
Xu, D.G.[Da-Guang],
Roth, H.R.[Holger R.],
Diminishing Uncertainty Within the Training Pool:
Active Learning for Medical Image Segmentation,
MedImg(40), No. 10, October 2021, pp. 2534-2547.
IEEE DOI
2110
Image segmentation, Biomedical imaging, Deep learning, Training,
Task analysis, Mutual information, Hippocampus, Deep Learning,
SVGD
BibRef
Feng, R.[Ruiwei],
Zheng, X.S.[Xiang-Shang],
Gao, T.X.[Tian-Xiang],
Chen, J.T.[Jin-Tai],
Wang, W.Z.[Wen-Zhe],
Chen, D.Z.[Danny Z.],
Wu, J.[Jian],
Interactive Few-Shot Learning:
Limited Supervision, Better Medical Image Segmentation,
MedImg(40), No. 10, October 2021, pp. 2575-2588.
IEEE DOI
2110
Image segmentation, Task analysis, Biomedical imaging, Training,
Annotations, Deep learning, Optimization, limited supervision
BibRef
Cui, H.J.[Heng-Ji],
Wei, D.[Dong],
Ma, K.[Kai],
Gu, S.[Shi],
Zheng, Y.F.[Ye-Feng],
A Unified Framework for Generalized Low-Shot Medical Image
Segmentation With Scarce Data,
MedImg(40), No. 10, October 2021, pp. 2656-2671.
IEEE DOI
2110
Image segmentation, Biomedical imaging, Training, Annotations,
Task analysis, Diseases, adaptive mixing coefficients
BibRef
Wang, L.[Lu],
Guo, D.[Dong],
Wang, G.[Guotai],
Zhang, S.T.[Shao-Ting],
Annotation-Efficient Learning for Medical Image Segmentation Based on
Noisy Pseudo Labels and Adversarial Learning,
MedImg(40), No. 10, October 2021, pp. 2795-2807.
IEEE DOI
2110
Image segmentation, Training, Annotations, Shape, Biomedical imaging,
Noise measurement, Deep learning, Segmentation, deep learning,
noisy labels
BibRef
Qu, L.[Lei],
Wang, M.[Meng],
Guo, K.X.[Kai-Xuan],
Wan, W.[Wan],
Liu, Y.[Yu],
Tang, J.[Jun],
Wu, J.[Jun],
Duan, P.[Peng],
Biomedical image segmentation based on full-Resolution network,
PRL(153), 2022, pp. 232-238.
Elsevier DOI
2201
Image Segmentation, Biomedical Image, Full-resolution,
Convolutional Neural Network
BibRef
Wang, R.S.[Ri-Sheng],
Lei, T.[Tao],
Cui, R.X.[Rui-Xia],
Zhang, B.T.[Bing-Tao],
Meng, H.Y.[Hong-Ying],
Nandi, A.K.[Asoke K.],
Medical image segmentation using deep learning: A survey,
IET-IPR(16), No. 5, 2022, pp. 1243-1267.
DOI Link
2203
Survey, Medical Images.
BibRef
Minaee, S.[Shervin],
Boykov, Y.Y.[Yuri Y.],
Porikli, F.M.[Fatih M.],
Plaza, A.[Antonio],
Kehtarnavaz, N.[Nasser],
Terzopoulos, D.[Demetri],
Image Segmentation Using Deep Learning: A Survey,
PAMI(44), No. 7, July 2022, pp. 3523-3542.
IEEE DOI
2206
Image segmentation, Semantics,
Deep learning, Computational modeling,
medical image segmentation
BibRef
Zhang, Z.X.[Zhen-Xi],
Tian, C.[Chunna],
Gao, X.B.[Xin-Bo],
Li, J.[Jie],
Jiao, Z.C.[Zhi-Cheng],
Wang, C.[Cui],
Zhong, Z.[Zhusi],
Collaborative boundary-aware context encoding networks for error map
prediction,
PR(125), 2022, pp. 108515.
Elsevier DOI
2203
Segmentation quality assessment, Error map prediction,
Medical image segmentation
BibRef
Gut, D.[Daniel],
Tabor, Z.[Zbislaw],
Szymkowski, M.[Mateusz],
Rozynek, M.[Milosz],
Kucybala, I.[Iwona],
Wojciechowski, W.[Wadim],
Benchmarking of Deep Architectures for Segmentation of Medical Images,
MedImg(41), No. 11, November 2022, pp. 3231-3241.
IEEE DOI
2211
Image segmentation, Task analysis, Training, Biomedical imaging,
Computed tomography, Magnetic resonance imaging, Benchmark, segmentation
BibRef
Zhang, Y.C.[Yun-Chu],
Dong, J.F.[Jian-Fei],
2K-Fold-Net and feature enhanced 4-Fold-Net for medical image
segmentation,
PR(127), 2022, pp. 108625.
Elsevier DOI
2205
2K-Fold-Net, EF-Net, U-Net, AFE, Image segmentation
BibRef
Liu, Z.H.[Zi-Hao],
Li, Z.W.[Zhuo-Wei],
Hu, Z.Q.[Zhi-Qiang],
Xia, Q.[Qing],
Xiong, R.Q.[Rui-Qin],
Zhang, S.T.[Shao-Ting],
Jiang, T.T.[Ting-Ting],
Contrastive and Selective Hidden Embeddings for Medical Image
Segmentation,
MedImg(41), No. 11, November 2022, pp. 3398-3410.
IEEE DOI
2211
Code, Segmentation.
WWW Link. Uncertainty, Image segmentation, Training, Task analysis,
Medical diagnostic imaging, Decoding, neural network
BibRef
Song, J.H.[Jia-Huan],
Chen, X.J.[Xin-Jian],
Zhu, Q.[Qianlong],
Shi, F.[Fei],
Xiang, D.[Dehui],
Chen, Z.Y.[Zhong-Yue],
Fan, Y.[Ying],
Pan, L.J.[Ling-Jiao],
Zhu, W.F.[Wei-Fang],
Global and Local Feature Reconstruction for Medical Image
Segmentation,
MedImg(41), No. 9, September 2022, pp. 2273-2284.
IEEE DOI
2209
Feature extraction, Image reconstruction, Semantics,
Image segmentation, Convolution, Biomedical imaging, Task analysis,
local feature reconstruction module
BibRef
Nan, Y.[Yang],
Tang, P.[Peng],
Zhang, G.[Guyue],
Zeng, C.H.[Cai-Hong],
Liu, Z.H.[Zhi-Hong],
Gao, Z.F.[Zhi-Fan],
Zhang, H.[Heye],
Yang, G.[Guang],
Unsupervised Tissue Segmentation via Deep Constrained Gaussian
Network,
MedImg(41), No. 12, December 2022, pp. 3799-3811.
IEEE DOI
2212
Image segmentation, Pathology, Training, Manuals, Annotations,
Deep learning, Unsupervised learning, Semantic segmentation,
tissue segmentation
BibRef
Zheng, R.F.[Rui-Feng],
Zhong, Y.[Ying],
Yan, S.[Senxiang],
Sun, H.C.[Hong-Cheng],
Shen, H.B.[Hai-Bin],
Huang, K.[Kejie],
MsVRL: Self-Supervised Multiscale Visual Representation Learning via
Cross-Level Consistency for Medical Image Segmentation,
MedImg(42), No. 1, January 2023, pp. 91-102.
IEEE DOI
2301
Image segmentation, Task analysis, Visualization,
Self-supervised learning, Medical diagnostic imaging, abdomen
BibRef
Hu, S.S.[Shi-Shuai],
Liao, Z.[Zehui],
Zhang, J.P.[Jian-Peng],
Xia, Y.[Yong],
Domain and Content Adaptive Convolution Based Multi-Source Domain
Generalization for Medical Image Segmentation,
MedImg(42), No. 1, January 2023, pp. 233-244.
IEEE DOI
2301
Image segmentation, Adaptation models, Head, Biomedical imaging,
Convolution, Training, Data models, Domain generalization,
deep learning
BibRef
Valverde, J.M.[Juan Miguel],
Tohka, J.[Jussi],
Region-wise loss for biomedical image segmentation,
PR(136), 2023, pp. 109208.
Elsevier DOI
2301
Deep learning, Segmentation, Medical imaging, Loss function
BibRef
Yuan, F.N.[Fei-Niu],
Zhang, Z.X.[Zheng-Xiao],
Fang, Z.J.[Zhi-Jun],
An effective CNN and Transformer complementary network for medical
image segmentation,
PR(136), 2023, pp. 109228.
Elsevier DOI
2301
Transformer, Medical image segmentation,
Feature complementary module, Cross-domain fusion, Convolutional Neural Network
BibRef
Li, S.[Shumeng],
Cai, H.[Heng],
Qi, L.[Lei],
Yu, Q.[Qian],
Shi, Y.H.[Ying-Huan],
Gao, Y.[Yang],
PLN: Parasitic-Like Network for Barely Supervised Medical Image
Segmentation,
MedImg(42), No. 3, March 2023, pp. 582-593.
IEEE DOI
2303
Annotations, Image segmentation, Training, Biomedical imaging,
Task analysis, Shape, 3D medical image segmentation,
parasitic-like network
BibRef
Cai, H.[Heng],
Li, S.[Shumeng],
Qi, L.[Lei],
Yu, Q.[Qian],
Shi, Y.H.[Ying-Huan],
Gao, Y.[Yang],
Orthogonal Annotation Benefits Barely-supervised Medical Image
Segmentation,
CVPR23(3302-3311)
IEEE DOI
2309
BibRef
Zou, W.X.[Wen-Xuan],
Qi, X.Q.[Xing-Qun],
Zhou, W.T.[Wan-Ting],
Sun, M.[Muyi],
Sun, Z.A.[Zhen-An],
Shan, C.F.[Cai-Feng],
Graph Flow: Cross-Layer Graph Flow Distillation for Dual Efficient
Medical Image Segmentation,
MedImg(42), No. 4, April 2023, pp. 1159-1171.
IEEE DOI
2304
Image segmentation, Knowledge engineering,
Medical diagnostic imaging, Sun, Cross layer design, Annotations, graph flow
BibRef
Yuan, C.[Chao],
Wang, Y.B.[Yan-Bo],
Xiao, Y.X.[Yun-Xuan],
LSUnetMix: Fuse channel feature information with long-short term
memory,
IET-CV(17), No. 2, 2023, pp. 241-249.
DOI Link
2304
biomedical engineering, image segmentation,
learning (artificial intelligence)
BibRef
Wang, K.[Kun],
Zhang, X.H.[Xiao-Hong],
Lu, Y.T.[Yu-Ting],
Zhang, W.[Wei],
Huang, S.[Sheng],
Yang, D.[Dan],
GSAL: Geometric structure adversarial learning for robust medical
image segmentation,
PR(140), 2023, pp. 109596.
Elsevier DOI
2305
Medical image segmentation, Geometric structure learning,
Adversarial learning, Computer-Aided diagnosis (CAD)
BibRef
Li, H.[He],
Iwamoto, Y.[Yutaro],
Han, X.H.[Xian-Hua],
Lin, L.F.[Lan-Fen],
Furukawa, A.[Akira],
Kanasaki, S.[Shuzo],
Chen, Y.W.[Yen-Wei],
3D Multiple-Contextual ROI-Attention Network for Efficient and Accurate
Volumetric Medical Image Segmentation,
IEICE(E106-D), No. 5, May 2023, pp. 1027-1037.
WWW Link.
2305
BibRef
Fang, W.H.[Wen-Hao],
Han, X.H.[Xian-Hua],
Spatial and Channel Attention Modulated Network for Medical Image
Segmentation,
MLCSA20(3-17).
Springer DOI
2103
BibRef
Song, Y.Y.[You-Yi],
Yu, L.Q.[Le-Quan],
Lei, B.Y.[Bai-Ying],
Choi, K.S.[Kup-Sze],
Qin, J.[Jing],
Data Discernment for Affordable Training in Medical Image
Segmentation,
MedImg(42), No. 5, May 2023, pp. 1431-1445.
IEEE DOI
2305
Training, Image segmentation, Biomedical imaging, Task analysis,
Training data, Programming, Annotations, Data discernment,
medical image segmentation
BibRef
Huang, X.H.[Xiao-Hong],
Deng, Z.F.[Zhi-Fang],
Li, D.D.[Dan-Dan],
Yuan, X.G.[Xue-Guang],
Fu, Y.[Ying],
MISSFormer: An Effective Transformer for 2D Medical Image
Segmentation,
MedImg(42), No. 5, May 2023, pp. 1484-1494.
IEEE DOI
2305
Transformers, Image segmentation, Task analysis, Bridges,
Medical diagnostic imaging, Feature extraction, Merging,
segmentation
BibRef
Hao, D.[Dechen],
Li, H.L.[Hua-Ling],
A graph-based edge attention gate medical image segmentation method,
IET-IPR(17), No. 7, 2023, pp. 2142-2157.
DOI Link
2305
dropout residual graph convolution block, edge attention gate,
medical image segmentation, UNet++, weighted loss function
BibRef
Li, Z.[Zeju],
Kamnitsas, K.[Konstantinos],
Ouyang, C.[Cheng],
Chen, C.[Chen],
Glocker, B.[Ben],
Context Label Learning: Improving Background Class Representations in
Semantic Segmentation,
MedImg(42), No. 6, June 2023, pp. 1885-1896.
IEEE DOI
2306
Image segmentation, Tumors, Task analysis, Liver, Kidney, Context modeling,
Training, Underfitting, multi-task learning, image segmentation
BibRef
Fang, C.W.[Chao-Wei],
Wang, Q.[Qian],
Cheng, L.[Lechao],
Gao, Z.F.[Zhi-Fan],
Pan, C.W.[Cheng-Wei],
Cao, Z.[Zhen],
Zheng, Z.H.[Zhao-Hui],
Zhang, D.W.[Ding-Wen],
Reliable Mutual Distillation for Medical Image Segmentation Under
Imperfect Annotations,
MedImg(42), No. 6, June 2023, pp. 1720-1734.
IEEE DOI
2306
Image segmentation, Reliability, Annotations, Noise measurement,
Data models, Training, Cleaning, Imperfect annotation,
medical image segmentation
BibRef
Xian, J.L.[Jun-Lin],
Li, X.[Xiang],
Tu, D.D.[Dan-Dan],
Zhu, S.[Senhua],
Zhang, C.Z.[Chang-Zheng],
Liu, X.W.[Xiao-Wu],
Li, X.[Xin],
Yang, X.[Xin],
Unsupervised Cross-Modality Adaptation via Dual Structural-Oriented
Guidance for 3D Medical Image Segmentation,
MedImg(42), No. 6, June 2023, pp. 1774-1785.
IEEE DOI
2306
Image segmentation, Biomedical imaging, Feature extraction,
Training, Magnetic resonance imaging, Computed tomography,
structural-oriented guidance
BibRef
Shu, Y.C.[Yu-Cheng],
Li, H.[Hengbo],
Xiao, B.[Bin],
Bi, X.L.[Xiu-Li],
Li, W.S.[Wei-Sheng],
Cross-Mix Monitoring for Medical Image Segmentation With Limited
Supervision,
MultMed(25), 2023, pp. 1700-1712.
IEEE DOI
2306
Image segmentation, Biomedical imaging, Training, Data models,
Perturbation methods, Task analysis, Monitoring, transductive monitor
BibRef
Ta, N.[Na],
Chen, H.P.[Hai-Peng],
Lyu, Y.D.[Ying-Da],
Wang, X.[Xue],
Shi, Z.[Zenan],
Liu, Z.H.[Zhe-Hao],
A complementary and contrastive network for stimulus segmentation and
generalization,
IVC(135), 2023, pp. 104694.
Elsevier DOI
2306
Complementary feature, Contrastive feature, Mutual attention,
Synthetic data augmentation, Medical image segmentation
BibRef
Jiang, M.[Meirui],
Yang, H.Z.[Hong-Zheng],
Cheng, C.[Chen],
Dou, Q.[Qi],
IOP-FL: Inside-Outside Personalization for Federated Medical Image
Segmentation,
MedImg(42), No. 7, July 2023, pp. 2106-2117.
IEEE DOI
2307
Adaptation models, Data models, Training, Routing,
Image segmentation, Biomedical imaging, Task analysis,
data heterogeneity
BibRef
Gao, Z.[Zheyao],
Wu, F.[Fuping],
Gao, W.G.[Wei-Guo],
Zhuang, X.[Xiahai],
A New Framework of Swarm Learning Consolidating Knowledge From
Multi-Center Non-IID Data for Medical Image Segmentation,
MedImg(42), No. 7, July 2023, pp. 2118-2129.
IEEE DOI
2307
Image segmentation, Training, Data models, Task analysis,
Biomedical imaging, Distributed databases, Distance learning,
swarm learning
BibRef
Lin, X.[Xian],
Yu, L.[Li],
Cheng, K.T.[Kwang-Ting],
Yan, Z.Q.[Zeng-Qiang],
The Lighter the Better: Rethinking Transformers in Medical Image
Segmentation Through Adaptive Pruning,
MedImg(42), No. 8, August 2023, pp. 2325-2337.
IEEE DOI
2308
Transformers, Biomedical imaging, Image segmentation, Training,
Task analysis, Computational complexity, Costs, Adaptive pruning, transformer
BibRef
Huang, S.Q.[Shi-Qi],
Xu, T.F.[Ting-Fa],
Shen, N.[Ning],
Mu, F.[Feng],
Li, J.A.[Jian-An],
Rethinking Few-Shot Medical Segmentation: A Vector Quantization View,
CVPR23(3072-3081)
IEEE DOI
2309
BibRef
Chen, Y.Z.[Yi-Zheng],
Yu, L.Q.[Le-Quan],
Wang, J.Y.[Jen-Yeu],
Panjwani, N.[Neil],
Obeid, J.P.[Jean-Pierre],
Liu, W.[Wu],
Liu, L.L.[Lian-Li],
Kovalchuk, N.[Nataliya],
Gensheimer, M.F.[Michael Francis],
Vitzthum, L.K.[Lucas Kas],
Beadle, B.M.[Beth M.],
Chang, D.T.[Daniel T.],
Le, Q.T.[Quynh-Thu],
Han, B.[Bin],
Xing, L.[Lei],
Adaptive Region-Specific Loss for Improved Medical Image Segmentation,
PAMI(45), No. 11, November 2023, pp. 13408-13421.
IEEE DOI
2310
BibRef
He, A.[Along],
Wang, K.[Kai],
Li, T.[Tao],
Du, C.K.[Cheng-Kun],
Xia, S.[Shuang],
Fu, H.Z.[Hua-Zhu],
H2Former: An Efficient Hierarchical Hybrid Transformer for Medical
Image Segmentation,
MedImg(42), No. 9, September 2023, pp. 2763-2775.
IEEE DOI
2310
BibRef
Wang, N.[Nan],
Lin, S.H.[Shao-Hui],
Li, X.X.[Xiao-Xiao],
Li, K.[Ke],
Shen, Y.[Yunhang],
Gao, Y.[Yue],
Ma, L.Z.[Li-Zhuang],
MISSU: 3D Medical Image Segmentation via Self-Distilling TransUNet,
MedImg(42), No. 9, September 2023, pp. 2740-2750.
IEEE DOI
2310
BibRef
Du, H.[Hao],
Dong, Q.H.[Qi-Hua],
Xu, Y.[Yan],
Liao, J.[Jing],
Weakly-Supervised 3D Medical Image Segmentation Using Geometric Prior
and Contrastive Similarity,
MedImg(42), No. 10, October 2023, pp. 2936-2947.
IEEE DOI
2310
BibRef
Zhang, J.Y.[Jing-Yang],
Gu, R.[Ran],
Xue, P.[Peng],
Liu, M.X.[Mian-Xin],
Zheng, H.[Hao],
Zheng, Y.F.[Ye-Feng],
Ma, L.[Lei],
Wang, G.[Guotai],
Gu, L.[Lixu],
S3R: Shape and Semantics-Based Selective Regularization for
Explainable Continual Segmentation Across Multiple Sites,
MedImg(42), No. 9, September 2023, pp. 2539-2551.
IEEE DOI
2310
BibRef
Xu, M.C.[Mou-Cheng],
Zhou, Y.K.[Yu-Kun],
Jin, C.[Chen],
de Groot, M.[Marius],
Alexander, D.C.[Daniel C.],
Oxtoby, N.P.[Neil P.],
Jacob, J.[Joseph],
MisMatch: Calibrated Segmentation via Consistency on Differential
Morphological Feature Perturbations With Limited Labels,
MedImg(42), No. 10, October 2023, pp. 2988-2999.
IEEE DOI
2310
BibRef
Wang, S.S.[Sheng-Sheng],
Fu, Z.[Zihao],
Wang, B.L.[Bi-Lin],
Hu, Y.L.[Yu-Long],
Fusing feature and output space for unsupervised domain adaptation on
medical image segmentation,
IJIST(33), No. 5, 2023, pp. 1672-1681.
DOI Link
2310
adversarial domain adaptation, domain adaptation,
image segmentation, medical image
BibRef
Pang, S.C.[Shu-Chao],
Du, A.[Anan],
Orgun, M.A.[Mehmet A.],
Wang, Y.[Yan],
Sheng, Q.Z.[Quan Z.],
Wang, S.J.[Shou-Jin],
Huang, X.S.[Xiao-Shui],
Yu, Z.M.[Zhen-Mei],
Beyond CNNs:
Exploiting Further Inherent Symmetries in Medical Image Segmentation,
Cyber(53), No. 11, November 2023, pp. 6776-6787.
IEEE DOI
2310
BibRef
Wu, H.M.[Hui-Min],
Li, X.M.[Xiao-Meng],
Lin, Y.Q.[Yi-Qun],
Cheng, K.T.[Kwang-Ting],
Compete to Win: Enhancing Pseudo Labels for Barely-Supervised Medical
Image Segmentation,
MedImg(42), No. 11, November 2023, pp. 3244-3255.
IEEE DOI Code:
WWW Link.
2311
BibRef
Li, Z.[Zeju],
Kamnitsas, K.[Konstantinos],
Dou, Q.[Qi],
Qin, C.[Chen],
Glocker, B.[Ben],
Joint Optimization of Class-Specific Training- and Test-Time Data
Augmentation in Segmentation,
MedImg(42), No. 11, November 2023, pp. 3323-3335.
IEEE DOI Code:
WWW Link.
2311
BibRef
Ji, Q.L.[Qiu-Lang],
Wang, J.H.[Ji-Hong],
Ding, C.[Caifu],
Wang, Y.H.[Yu-Hang],
Zhou, W.[Wen],
Liu, Z.J.[Zi-Jie],
Yang, C.[Chen],
DMAGNet: Dual-path multi-scale attention guided network for medical
image segmentation,
IET-IPR(17), No. 13, 2023, pp. 3631-3644.
DOI Link
2311
codecs, convolutional neural nets, image processing, image segmentation
BibRef
Chen, J.K.[Jing-Kun],
Chen, C.R.[Chang-Rui],
Huang, W.J.[Wen-Jian],
Zhang, J.G.[Jian-Guo],
Debattista, K.[Kurt],
Han, J.G.[Jun-Gong],
Dynamic contrastive learning guided by class confidence and confusion
degree for medical image segmentation,
PR(145), 2024, pp. 109881.
Elsevier DOI
2311
Class confusion degree, Dynamic contrastive learning, Medical image segmentation
BibRef
Qiu, Z.X.[Zhong-Xi],
Hu, Y.[Yan],
Chen, X.S.[Xiao-Shan],
Zeng, D.[Dan],
Hu, Q.Y.[Qing-Yong],
Liu, J.[Jiang],
Rethinking Dual-Stream Super-Resolution Semantic Learning in Medical
Image Segmentation,
PAMI(46), No. 1, January 2024, pp. 451-464.
IEEE DOI
2312
BibRef
Zhang, J.J.[Jiao-Jiao],
Zhang, S.[Shuo],
Shen, X.Q.[Xiao-Qian],
Lukasiewicz, T.[Thomas],
Xu, Z.H.[Zheng-Hua],
Multi-ConDoS: Multimodal Contrastive Domain Sharing Generative
Adversarial Networks for Self-Supervised Medical Image Segmentation,
MedImg(43), No. 1, January 2024, pp. 76-95.
IEEE DOI
2401
BibRef
Xu, Z.H.[Zheng-Hua],
Liu, Y.X.[Yun-Xin],
Xu, G.[Gang],
Lukasiewicz, T.[Thomas],
Self-Supervised Medical Image Segmentation Using Deep Reinforced
Adaptive Masking,
MedImg(44), No. 1, January 2025, pp. 180-193.
IEEE DOI
2501
Biomedical imaging, Image reconstruction, Image segmentation,
Task analysis, Adaptation models, Self-supervised learning,
deep reinforcement learning
BibRef
Lei, W.H.[Wen-Hui],
Su, Q.[Qi],
Jiang, T.Y.[Tian-Yu],
Gu, R.[Ran],
Wang, N.[Na],
Liu, X.L.[Xing-Long],
Wang, G.[Guotai],
Zhang, X.F.[Xiao-Fan],
Zhang, S.T.[Shao-Ting],
One-Shot Weakly-Supervised Segmentation in 3D Medical Images,
MedImg(43), No. 1, January 2024, pp. 175-189.
IEEE DOI Code:
WWW Link.
2401
BibRef
Wang, J.C.[Jia-Cheng],
Jin, Y.M.[Yue-Ming],
Stoyanov, D.[Danail],
Wang, L.S.[Lian-Sheng],
FedDP: Dual Personalization in Federated Medical Image Segmentation,
MedImg(43), No. 1, January 2024, pp. 297-308.
IEEE DOI Code:
WWW Link.
2401
BibRef
Earlier: A1, A2, A4, Only:
Personalizing Federated Medical Image Segmentation via Local
Calibration,
ECCV22(XXI:456-472).
Springer DOI
2211
BibRef
Tong, S.S.[Shan-Shan],
Zuo, Z.T.[Zhen-Tao],
Liu, Z.[Zuxiang],
Sun, D.[Dengdi],
Zhou, T.G.[Tian-Gang],
Hybrid attention mechanism of feature fusion for medical image
segmentation,
IET-IPR(18), No. 1, 2024, pp. 77-87.
DOI Link
2401
biomedical imaging, image segmentation, medical image processing
BibRef
Lu, C.Z.[Cheng-Zhun],
Xia, Z.R.[Zhang-Run],
Przystupa, K.[Krzysztof],
Kochan, O.[Orest],
Su, J.[Jun],
DCELANM-Net: Medical image segmentation based on dual channel
efficient layer aggregation network with learner,
IJIST(34), No. 1, 2024, pp. e22960.
DOI Link
2401
CNN, medical image segmentation, self-supervised learning, transformer
BibRef
Ming, Q.[Qi],
Xiao, X.W.[Xiao-Wu],
Towards Accurate Medical Image Segmentation with Gradient-Optimized
Dice Loss,
SPLetters(31), 2024, pp. 191-195.
IEEE DOI
2401
BibRef
Pang, Y.[Yan],
Liang, J.M.[Jia-Ming],
Huang, T.[Teng],
Chen, H.[Hao],
Li, Y.H.[Yun-Hao],
Li, D.[Dan],
Huang, L.[Lin],
Wang, Q.[Qiong],
Slim UNETR: Scale Hybrid Transformers to Efficient 3D Medical Image
Segmentation Under Limited Computational Resources,
MedImg(43), No. 3, March 2024, pp. 994-1005.
IEEE DOI Code:
WWW Link.
2403
Biomedical imaging, Transformers, Image segmentation, Task analysis,
Computational modeling, Solid modeling, resource-limited application
BibRef
Han, X.J.[Xian-Jun],
Li, T.T.[Tian-Tian],
Bai, C.[Can],
Yang, H.Y.[Hong-Yu],
Integrating prior knowledge into a bibranch pyramid network for
medical image segmentation,
IVC(143), 2024, pp. 104945.
Elsevier DOI
2403
Image pyramid, Medical image segmentation, Prior knowledge,
Medical image processing
BibRef
Li, H.[Hao],
Zhai, D.H.[Di-Hua],
Xia, Y.Q.[Yuan-Qing],
ERDUnet: An Efficient Residual Double-Coding Unet for Medical Image
Segmentation,
CirSysVideo(34), No. 4, April 2024, pp. 2083-2096.
IEEE DOI Code:
WWW Link.
2404
Image segmentation, Feature extraction,
Medical diagnostic imaging, Lesions, Transformers, Encoding,
reduce parameter scale
BibRef
Huang, Z.M.[Zhong-Miao],
Cheng, S.[Shuli],
Wang, L.J.[Lie-Jun],
Medical image segmentation based on dynamic positioning and
region-aware attention,
PR(151), 2024, pp. 110375.
Elsevier DOI
2404
Medical image segmentation, Transformer,
Dynamic Positioning Attention, Bi-Level Routing Attention
BibRef
Guo, X.[Xiayu],
Lin, X.[Xian],
Yang, X.[Xin],
Yu, L.[Li],
Cheng, K.T.[Kwang-Ting],
Yan, Z.Q.[Zeng-Qiang],
UCTNet: Uncertainty-guided CNN-Transformer hybrid networks for
medical image segmentation,
PR(152), 2024, pp. 110491.
Elsevier DOI Code:
WWW Link.
2405
CNN-Transformer hybrid, Uncertainty, Functional overlap,
Masked self-attention, Medical image segmentation
BibRef
Liu, X.Q.[Xiao-Qing],
Ono, K.[Kenji],
Bise, R.[Ryoma],
A data augmentation approach that ensures the reliability of
foregrounds in medical image segmentation,
IVC(147), 2024, pp. 105056.
Elsevier DOI
2406
Medical image analysis, Medical image segmentation, Data augmentation
BibRef
Wang, Z.B.[Zhi-Bing],
Wang, W.M.[Wen-Min],
Li, N.N.[Nan-Nan],
Zhang, S.Y.[Shen-Yong],
Chen, Q.[Qi],
Jiang, Z.[Zhe],
Multimodal parallel attention network for medical image segmentation,
IVC(147), 2024, pp. 105069.
Elsevier DOI
2406
Multimodal parallel attention, Feature parallel,
Spatial parallel, Channel parallel, Medical image segmentation
BibRef
Zhu, E.[Enjun],
Feng, H.[Haiyu],
Chen, L.[Long],
Lai, Y.Q.[Yong-Qiang],
Chai, S.[Senchun],
MP-Net: A Multi-Center Privacy-Preserving Network for Medical Image
Segmentation,
MedImg(43), No. 7, July 2024, pp. 2718-2729.
IEEE DOI
2407
Biomedical imaging, Encryption, Image segmentation, Training, Cryptography,
Hospitals, Medical diagnostic imaging, Deep learning, segmentation
BibRef
Chen, K.[Kecheng],
Qin, T.[Tiexin],
Lee, V.H.F.[Victor Ho-Fun],
Yan, H.[Hong],
Li, H.L.[Hao-Liang],
Learning Robust Shape Regularization for Generalizable Medical Image
Segmentation,
MedImg(43), No. 7, July 2024, pp. 2693-2706.
IEEE DOI
2407
Shape, Image segmentation, Biomedical imaging, Training,
Image edge detection, Feature extraction, knowledge distillation
BibRef
Yang, Z.Y.[Zhi-Yi],
Zhao, Z.[Zhou],
Gu, Y.L.[Yu-Liang],
Xu, Y.C.[Yong-Chao],
Query-guided generalizable medical image segmentation,
PRL(184), 2024, pp. 52-58.
Elsevier DOI
2408
Medical image segmentation, Domain generalized, Query-based transformer
BibRef
Huang, X.R.[Xing-Ru],
Huang, J.[Jian],
Zhao, K.[Kai],
Zhang, T.Y.[Tian-Yun],
Li, Z.[Zhi],
Yue, C.P.[Chang-Peng],
Chen, W.H.[Wen-Hao],
Wang, R.H.[Rui-Hao],
Chen, X.B.[Xuan-Bin],
Zhang, Q.[Qianni],
Fu, Y.[Ying],
Wang, Y.Y.[Yang-Yundou],
Guo, Y.H.[Yi-Hao],
SASAN: Spectrum-Axial Spatial Approach Networks for Medical Image
Segmentation,
MedImg(43), No. 8, August 2024, pp. 3044-3056.
IEEE DOI Code:
WWW Link.
2408
BibRef
Liu, Q.[Qing],
Zeng, H.L.[Hai-Long],
Sun, Z.D.[Zhao-Dong],
Li, X.B.[Xiao-Bai],
Zhao, G.Y.[Guo-Ying],
Liang, Y.X.[Yi-Xiong],
Many birds, one stone: Medical image segmentation with multiple
partially labeled datasets,
PR(155), 2024, pp. 110636.
Elsevier DOI Code:
WWW Link.
2408
Partially supervised learning, Self-training,
Medical image segmentation, Cross-task attention
BibRef
Fiaz, M.[Mustansar],
Noman, M.[Mubashir],
Cholakkal, H.[Hisham],
Anwer, R.M.[Rao Muhammad],
Hanna, J.[Jacob],
Khan, F.S.[Fahad Shahbaz],
Guided-attention and gated-aggregation network for medical image
segmentation,
PR(156), 2024, pp. 110812.
Elsevier DOI Code:
WWW Link.
2408
Medical image segmentation, Multi-scale feature aggregation,
Mask-guided feature attention, Deep supervision, Transformers,
Convolutional neural networks
BibRef
Gai, D.[Di],
Geng, Y.H.[Yu-Han],
Huang, X.[Xia],
Huang, Z.[Zheng],
Xiong, X.[Xin],
Zhou, R.H.[Rui-Hua],
Wang, Q.[Qi],
Feature ensemble network for medical image segmentation with
multi-scale atrous transformer,
IET-IPR(18), No. 11, 2024, pp. 3082-3092.
DOI Link
2409
biomedical imaging, image segmentation, medical image processing
BibRef
Wang, Y.[Ying],
Zhang, M.[Meng],
Liang, J.[Jian'an],
Liang, M.Y.[Mei-Yan],
MFH-Net: A Hybrid CNN-Transformer Network Based Multi-Scale Fusion
for Medical Image Segmentation,
IJIST(34), No. 6, 2024, pp. e23192.
DOI Link
2410
medical image segmentation, multi-scale feature fusion,
skip connection, U-Net
BibRef
Zhang, Y.M.[Yu-Min],
Li, H.L.[Hong-Liu],
Gao, Y.J.[Ya-Jun],
Duan, H.R.[Hao-Ran],
Huang, Y.W.[Ya-Wen],
Zheng, Y.F.[Ye-Feng],
Prototype Correlation Matching and Class- Relation Reasoning for
Few-Shot Medical Image Segmentation,
MedImg(43), No. 11, November 2024, pp. 4041-4054.
IEEE DOI
2411
Medical diagnostic imaging, Image segmentation, Correlation,
Prototypes, Task analysis, Cognition, Semantics,
inter-class relations
BibRef
Chen, T.[Tao],
Wang, C.[Chenhui],
Chen, Z.H.[Zhi-Hao],
Lei, Y.M.[Yi-Ming],
Shan, H.M.[Hong-Ming],
HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation,
MedImg(43), No. 10, October 2024, pp. 3570-3583.
IEEE DOI Code:
WWW Link.
2411
Image segmentation, Biomedical imaging, Diffusion models,
Task analysis, Data models, Training, Transformers,
diffusion model
BibRef
Ji, Z.X.[Ze-Xuan],
Ye, S.L.[Shun-Long],
Ma, X.[Xiao],
Sparse Coding Inspired LSTM and Self-Attention Integration for
Medical Image Segmentation,
IP(33), 2024, pp. 6098-6113.
IEEE DOI Code:
WWW Link.
2411
Long short term memory, Image coding, Image segmentation,
Medical diagnostic imaging, Sparse matrices, Codes,
medical image segmentation
BibRef
Zhang, J.W.[Jia-Wei],
Zhang, Y.C.[Yan-Chun],
Qiu, H.L.[Hai-Long],
Wang, T.C.[Tian-Chen],
Li, X.M.[Xiao-Meng],
Zhu, S.F.[Shan-Feng],
Huang, M.P.[Mei-Ping],
Zhuang, J.[Jian],
Shi, Y.Y.[Yi-Yu],
Xu, X.W.[Xiao-Wei],
Constrained multi-scale dense connections for biomedical image
segmentation,
PR(158), 2025, pp. 111031.
Elsevier DOI Code:
WWW Link.
2411
Multi-scale dense connections, Image segmentation,
Network architecture search, Feature fusion
BibRef
Sun, J.D.[Jun-Ding],
Li, Y.[Yabei],
Wu, X.S.[Xiao-Sheng],
Tang, C.S.[Chao-Sheng],
Wang, S.H.[Shui-Hua],
Zhang, Y.D.[Yu-Dong],
HAD-Net: An attention U-based network with hyper-scale shifted
aggregating and max-diagonal sampling for medical image segmentation,
CVIU(249), 2024, pp. 104151.
Elsevier DOI
2412
Medical image segmentation, Max-diagonal pooling,
Channel-space attention, Hyper-scale shifted aggregation
BibRef
Ning, G.J.[Gang-Jun],
Liu, P.P.[Ping-Ping],
Dai, C.Y.[Chuang-Ye],
Sun, M.[Mingsi],
Zhou, Q.Z.[Qiu-Zhan],
Li, Q.L.[Qing-Liang],
RGAM: A refined global attention mechanism for medical image
segmentation,
IET-CV(18), No. 8, 2024, pp. 1362-1375.
DOI Link
2501
convolutional neural nets, medical image processing
BibRef
Zhang, Z.[Zheng],
Yin, G.C.[Guan-Chun],
Ma, Z.[Zibo],
Tan, Y.P.[Yun-Peng],
Zhang, B.[Bo],
Zhuang, Y.F.[Yu-Feng],
IDA-NET: Individual Difference aware Medical Image Segmentation with
Meta-Learning,
PRL(187), 2025, pp. 21-27.
Elsevier DOI
2501
Transformer, U-Net, Meta Learning, Individual Difference,
Medical Image Segmentation
BibRef
Gao, J.[Jun],
Lao, Q.[Qicheng],
Kang, Q.B.[Qing-Bo],
Liu, P.[Paul],
Du, C.L.[Chen-Lin],
Li, K.[Kang],
Zhang, L.[Le],
Boosting Your Context by Dual Similarity Checkup for In-Context
Learning Medical Image Segmentation,
MedImg(44), No. 1, January 2025, pp. 310-319.
IEEE DOI
2501
Image segmentation, Biomedical imaging, Task analysis, Semantics,
Visualization, Data models, Computational modeling,
support set reinforcement
BibRef
Amaan-Valiuddin, M.M.,
Viviers, C.G.A.[Christiaan G. A.],
van Sloun, R.J.G.[Ruud J. G.],
de With, P.H.N.[Peter H. N.],
van der Sommen, F.[Fons],
Investigating and Improving Latent Density Segmentation Models for
Aleatoric Uncertainty Quantification in Medical Imaging,
MedImg(44), No. 1, January 2025, pp. 384-395.
IEEE DOI
2501
Uncertainty, Image segmentation, Probabilistic logic, Decoding,
Training, Biomedical imaging, Annotations,
latent density modeling
BibRef
Huang, W.D.[Wen-Dong],
Hu, J.[Jinwu],
Xiao, J.H.[Jun-Hao],
Wei, Y.[Yang],
Bi, X.L.[Xiu-Li],
Xiao, B.[Bin],
Prototype-Guided Graph Reasoning Network for Few-Shot Medical Image
Segmentation,
MedImg(44), No. 2, February 2025, pp. 761-773.
IEEE DOI
2502
Prototypes, Semantic segmentation, Cognition,
Medical diagnostic imaging, Training, Data models,
medical image segmentation
BibRef
Dhamale, A.[Akshat],
Rajalakshmi, R.[Ratnavel],
Balasundaram, A.[Ananthakrishnan],
Dual multi scale networks for medical image segmentation using
contrastive learning,
IVC(154), 2025, pp. 105371.
Elsevier DOI
2502
Contrastive learning, Multi-scale architecture,
Encoder-decoder based model, Medical image segmentation
BibRef
Wang, W.[Wei],
He, J.X.[Ji-Xing],
Wang, X.[Xin],
Rethinking Feature Guidance for Medical Image Segmentation,
SPLetters(32), 2025, pp. 641-645.
IEEE DOI
2502
Feature extraction, Image segmentation, Lesions,
Medical diagnostic imaging, Convolution, Logic gates, Transformers,
medical image segmentation
BibRef
Huang, S.[Senlong],
Ge, Y.X.[Yong-Xin],
Liu, D.F.[Dong-Fang],
Hong, M.J.[Ming-Jian],
Zhao, J.[Junhan],
Loui, A.C.[Alexander C.],
Rethinking Copy-Paste for Consistency Learning in Medical Image
Segmentation,
IP(34), 2025, pp. 1060-1074.
IEEE DOI Code:
WWW Link.
2502
Perturbation methods, Data models, Training, Image segmentation,
Medical diagnostic imaging, Uncertainty, Estimation, Training data,
copy-paste
BibRef
Huang, Y.Z.[Yun-Zhi],
Han, L.[Luyi],
Dou, H.R.[Hao-Ran],
Generative feature style augmentation for domain generalization in
medical image segmentation,
PR(162), 2025, pp. 111416.
Elsevier DOI
2503
Domain generalization, Variation inference, Feature style mapping
BibRef
Ren, Z.T.[Zi-Tong],
Li, Y.M.[Yong-Ming],
Wang, L.J.[Lie-Jun],
Xu, L.H.[Liang-Hui],
Lite-MixedNet: Lightweight and efficient hybrid network for medical
image segmentation,
PR(162), 2025, pp. 111378.
Elsevier DOI Code:
WWW Link.
2503
Medical image segmentation, Convolutional Neural Network,
Vision Transformer, Self-attention mechanism
BibRef
Wang, Z.H.[Zhi-Hua],
He, Y.X.[Yu-Xin],
Yi, Z.[Zhang],
He, T.[Tao],
Bu, J.J.[Jia-Jun],
Neural Memory Self-Supervised State Space Models With Learnable Gates,
SPLetters(32), 2025, pp. 926-930.
IEEE DOI
2503
Computational modeling, Logic gates, Image segmentation, Decoding,
Training, Image reconstruction, Head, Biomedical imaging,
self-supervision
BibRef
Lin, L.[Li],
Liu, Y.X.[Yi-Xiang],
Wu, J.W.[Jie-Wei],
Cheng, P.[Pujin],
Cai, Z.Y.[Zhi-Yuan],
Wong, K.K.Y.[Kenneth K. Y.],
Tang, X.Y.[Xiao-Ying],
FedLPPA: Learning Personalized Prompt and Aggregation for Federated
Weakly-Supervised Medical Image Segmentation,
MedImg(44), No. 3, March 2025, pp. 1127-1139.
IEEE DOI Code:
WWW Link.
2503
Image segmentation, Data models, Training, Annotations,
Computational modeling, Medical diagnostic imaging,
medical image segmentation
BibRef
Wang, H.F.[Hui-Fang],
Liu, Y.T.[Ya-Tong],
Ye, J.Y.[Jiong-Yao],
Yang, D.W.[Da-Wei],
Zhu, Y.[Yu],
TS-Net: Trans-Scale Network for Medical Image Segmentation,
IJIST(35), No. 2, 2025, pp. e70064.
DOI Link
2504
convolution modulation, deep supervision, edge loss,
feature complementarity, medical image segmentation
BibRef
Fanyang, Z.[Zhang],
Fan, Z.[Zhang],
CS U-NET: A Medical Image Segmentation Method Integrating Spatial and
Contextual Attention Mechanisms Based on U-NET,
IJIST(35), No. 2, 2025, pp. e70072.
DOI Link
2504
CBAM, deep learning, medical image segmentation,
Swin transformer, U-net
BibRef
Zhang, C.[Chuyan],
Zheng, H.[Hao],
You, X.[Xin],
Zheng, Y.F.[Ye-Feng],
Gu, Y.[Yun],
PASS: Test-Time Prompting to Adapt Styles and Semantic Shapes in
Medical Image Segmentation,
MedImg(44), No. 4, April 2025, pp. 1853-1865.
IEEE DOI Code:
WWW Link.
2504
Shape, Adaptation models, Training, Visualization,
Image segmentation, Data models, Semantics, Predictive models, transfer learning
BibRef
Gu, Y.Y.[Ying-Yan],
Wang, Y.[Yan],
Ye, H.[Hua],
Shu, X.[Xin],
DA-Net: Deep attention network for biomedical image segmentation,
SP:IC(135), 2025, pp. 117283.
Elsevier DOI
2504
Biomedical image segmentation, U-Net,
Improved triplet attention, Residual concatenate block
BibRef
Wu, L.[Lanhu],
Zhang, M.[Miao],
Piao, Y.[Yongri],
Yao, Z.Y.[Zhen-Yan],
Sun, W.B.[Wei-Bing],
Tian, F.[Feng],
Lu, H.C.[Hu-Chuan],
CNN-Transformer Rectified Collaborative Learning for Medical Image
Segmentation,
CirSysVideo(35), No. 5, May 2025, pp. 4072-4086.
IEEE DOI Code:
WWW Link.
2505
Transformers, Federated learning, Knowledge transfer,
Image segmentation, Convolutional neural networks, Accuracy,
collaborative learning
BibRef
Jing, W.P.[Wei-Peng],
Wang, J.[Junze],
Di, D.L.[Dong-Lin],
Li, D.D.[Dan-Dan],
Song, Y.[Yang],
Fan, L.[Lei],
Multi-modal hypergraph contrastive learning for medical image
segmentation,
PR(165), 2025, pp. 111544.
Elsevier DOI Code:
WWW Link.
2505
Multi-modal medical image, Self-supervised learning,
Hypergraph learning, Knowledge distillation
BibRef
Zhang, Z.Z.[Zhong-Zhou],
Chen, Y.Y.[Ying-Yu],
Yu, H.[Hui],
Wang, Z.W.[Zhi-Wen],
Wang, S.S.[Shan-Shan],
Fan, F.L.[Feng-Lei],
Shan, H.M.[Hong-Ming],
Zhang, Y.[Yi],
UniAda: Domain Unifying and Adapting Network for Generalizable
Medical Image Segmentation,
MedImg(44), No. 5, May 2025, pp. 1988-2001.
IEEE DOI Code:
WWW Link.
2505
Adaptation models, Training, Image segmentation, Testing,
Data models, Uncertainty, Perturbation methods, unifying and adapting
BibRef
Zhu, Y.[Yun],
Zhang, D.[Dong],
Lin, Y.[Yi],
Feng, Y.F.[Yi-Fei],
Tang, J.H.[Jin-Hui],
Merging Context Clustering With Visual State Space Models for Medical
Image Segmentation,
MedImg(44), No. 5, May 2025, pp. 2131-2142.
IEEE DOI Code:
WWW Link.
2505
Context modeling, Image segmentation, Computational modeling,
Visualization, Transformers, Medical diagnostic imaging,
visual state space model
BibRef
Chen, P.[Peng],
Wang, H.H.[Hui-Hui],
Jin, Q.[Qin],
MPKU-Net: A U-Shaped Medical Image Segmentation Network Based on MLP
and KAN,
IJIST(35), No. 3, 2025, pp. e70105.
DOI Link Code:
WWW Link.
2506
KAN network architecture, medical image segmentation,
multilayer perceptron, u-shaped network
BibRef
Wang, Y.B.[Yuan-Bin],
Shi, Y.[Yunbo],
Zhao, R.[Rui],
Chen, Y.[Yunan],
Ren, X.Q.[Xing-Qiao],
Xing, B.H.[Bing-Hong],
MGMFormer: Multi-Scale Attentional Medical Image Segmentation Network
for Semantic Feature Enhancement,
IJIST(35), No. 3, 2025, pp. e70086.
DOI Link
2506
attention mechanism, feature enhancement,
medical image segmentation, multi-scale feature extraction
BibRef
Yu, X.C.[Xiang-Chun],
Chen, Q.[Qiaoyi],
Liang, M.M.[Miao-Miao],
Yu, L.J.[Ling-Juan],
Zheng, J.[Jian],
Feature Subspace Projection Knowledge Distillation for Medical Image
Segmentation,
IJIST(35), No. 3, 2025, pp. e70085.
DOI Link
2506
bias-variance coupling,
conditional generative adversarial learning, medical image segmentation
BibRef
Tian, M.[Mu],
Yang, Q.Z.[Qin-Zhu],
Gao, Y.[Yi],
AttenScribble: Attention-enhanced scribble supervision for medical
image segmentation,
JVCIR(110), 2025, pp. 104476.
Elsevier DOI Code:
WWW Link.
2506
Weakly-supervised learning, Scribbles, Self-attention
BibRef
Bogensperger, L.[Lea],
Narnhofer, D.[Dominik],
Falk, A.[Alexander],
Schindler, K.[Konrad],
Pock, T.[Thomas],
FlowSDF: Flow Matching for Medical Image Segmentation Using Distance
Transforms,
IJCV(133), No. 7, July 2025, pp. 4864-4876.
Springer DOI
2506
BibRef
Chen, C.J.[Chia-Jui],
Hard-UNet architecture for medical image segmentation using position
encoding generator: LSA based encoder,
JVCIR(110), 2025, pp. 104452.
Elsevier DOI
2506
Medical image segmentation, Hardnet, LSA, Big data
BibRef
Shan, D.D.[Dan-Dan],
Li, Z.[Zihan],
Li, Y.X.[Yun-Xiang],
Li, Q.[Qingde],
Tian, J.[Jie],
Hong, Q.Q.[Qing-Qi],
STPNet: Scale-Aware Text Prompt Network for Medical Image
Segmentation,
IP(34), 2025, pp. 3169-3180.
IEEE DOI Code:
WWW Link.
2506
Image segmentation, Lesions, Feature extraction, Accuracy,
Visualization, Medical diagnostic imaging, Training, Lungs,
medical image segmentation
BibRef
Chen, J.Y.[Jing-Yun],
Yuan, Y.D.[Ya-Ding],
Decentralized Personalization for Federated Medical Image
Segmentation via Gossip Contrastive Mutual Learning,
MedImg(44), No. 7, July 2025, pp. 2768-2783.
IEEE DOI Code:
WWW Link.
2507
Data models, Servers, Predictive models, Biomedical imaging,
Adaptation models, Training, Image segmentation, Brain modeling,
automated tumor segmentation
BibRef
Wang, H.R.[Hao-Ran],
Huai, L.[Lian],
Li, W.B.[Wen-Bin],
Qi, L.[Lei],
Jiang, X.Q.[Xing-Qun],
Shi, Y.H.[Ying-Huan],
WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM With
Sub-Class Exploration and Prompt Affinity Mining,
MedImg(44), No. 7, July 2025, pp. 2795-2807.
IEEE DOI Code:
WWW Link.
2507
Image segmentation, Biomedical imaging, Training, Tumors,
Data mining, Adaptation models, Weak supervision, Head,
prompt affinity mining
BibRef
Wang, J.N.[Jiang-Nan],
Zhou, C.X.[Cai-Xia],
Huang, Y.P.[Ya-Ping],
Contour-Aware Multi-Expert Model for Ambiguous Medical Image
Segmentation,
MedImg(44), No. 8, August 2025, pp. 3284-3298.
IEEE DOI
2508
Image segmentation, Uncertainty, Stochastic processes, Accuracy,
Annotations, Probabilistic logic, Predictive models,
uncertainty estimation
BibRef
Chen, T.X.[Tian-Xiang],
Zhou, X.D.[Xu-Dong],
Tan, Z.T.[Zhen-Tao],
Wu, Y.[Yue],
Wang, Z.Y.[Zi-Yang],
Ye, Z.[Zi],
Gong, T.[Tao],
Chu, Q.[Qi],
Yu, N.H.[Neng-Hai],
Lu, L.[Le],
Zig-RiR: Zigzag RWKV-in-RWKV for Efficient Medical Image Segmentation,
MedImg(44), No. 8, August 2025, pp. 3245-3257.
IEEE DOI Code:
WWW Link.
2508
Biomedical imaging, Image segmentation, Visualization, Feature extraction,
Computational modeling, Accuracy, Transformers, zigzag scan
BibRef
Chen, H.[Hejian],
Liu, Q.[Qing],
Fu, Z.M.[Zhong-Ming],
Liu, L.[Li],
UM-Mamba: An efficient U-network with medical visual state space for
medical image segmentation,
CVIU(259), 2025, pp. 104436.
Elsevier DOI
2509
Mamba, Medical image segmentation, State space models, U-net
BibRef
Liao, M.[Miao],
Yang, R.X.[Rui-Xin],
Zhao, Y.Q.[Yu-Qian],
Liang, W.[Wei],
Yuan, J.S.[Jun-Song],
FocalTransNet: A Hybrid Focal-Enhanced Transformer Network for
Medical Image Segmentation,
IP(34), 2025, pp. 5614-5627.
IEEE DOI Code:
WWW Link.
2509
Transformers, Iron, Biomedical imaging, Image segmentation,
Feature extraction, Decoding, Merging, downsampling
BibRef
Zhou, X.L.[Xiao-Ling],
Wu, S.[Shili],
Qiao, Y.[Yalu],
Guo, Y.K.[Yong-Kun],
Qian, C.[Chao],
Zhang, X.[Xinyou],
Research on Multi-Objective Optimization of Medical Image
Segmentation Based on Frequency Domain Decoupling and Dual Attention
Mechanism,
IJIST(35), No. 5, 2025, pp. e70186.
DOI Link Code:
WWW Link.
2509
dual-frequency multiscale fusion, medical image segmentation,
multiscale dual attention, sublinear computation
BibRef
Wan, Y.L.[Yu-Long],
Zhou, D.M.[Dong-Ming],
Yan, R.[Ran],
EGLC: Enhancing Global Localization Capability for medical image
segmentation,
CVIU(260), 2025, pp. 104471.
Elsevier DOI
2510
Deep learning, Medical image segmentation, Wavelet transform, Transformer
BibRef
Cheng, Z.[Zihan],
Guo, J.T.[Jin-Tao],
Zhang, J.[Jian],
Qi, L.[Lei],
Zhou, L.P.[Lu-Ping],
Shi, Y.H.[Ying-Huan],
Gao, Y.[Yang],
Mamba-Sea: A Mamba-Based Framework With Global-to-Local Sequence
Augmentation for Generalizable Medical Image Segmentation,
MedImg(44), No. 9, September 2025, pp. 3741-3755.
IEEE DOI Code:
WWW Link.
2510
Image segmentation, Biomedical imaging, Training,
Adaptation models, Semantics, Feature extraction, Data models, Mamba
BibRef
Zhang, Z.Y.[Zhe-Yuan],
Yao, L.H.[Lan-Hong],
Wang, B.[Bin],
Jha, D.[Debesh],
Durak, G.[Gorkem],
Keles, E.[Elif],
Medetalibeyoglu, A.[Alpay],
Bagci, U.[Ulas],
DiffBoost: Enhancing Medical Image Segmentation via Text-Guided
Diffusion Model,
MedImg(44), No. 9, September 2025, pp. 3670-3682.
IEEE DOI Code:
WWW Link.
2510
Biomedical imaging, Diffusion models, Image segmentation,
Data augmentation, Data models, Training, Noise, Noise reduction,
diffusion models
BibRef
Noh, J.[Jeonghyun],
Jeon, W.[Wangsu],
Park, J.[Jinsun],
Dual interaction network with cross-image attention for medical image
segmentation,
PRL(197), 2025, pp. 332-338.
Elsevier DOI Code:
WWW Link.
2510
Dual interaction, Cross-image attention, Medical image segmentation
BibRef
Yu, R.[Ruiguo],
Zhang, Y.Y.[Yi-Yang],
Tian, Y.[Yuan],
Diao, Y.J.[Yu-Jie],
Jin, D.[Di],
Pedrycz, W.[Witold],
MAMBO-NET: Multi-causal aware modeling backdoor-intervention
optimization for medical image segmentation network,
PRL(197), 2025, pp. 102-109.
Elsevier DOI
2510
Medical image segmentation, Causal inference, Backdoor model, Gaussian modeling
BibRef
Luan, X.[Xiao],
Fu, Y.[Yule],
Liu, L.H.[Ling-Hui],
Li, W.S.[Wei-Sheng],
CSSNet: A 3D medical image segmentation network based on compressed
sparse dual-branch structure,
PRL(197), 2025, pp. 222-228.
Elsevier DOI
2510
Medical image segmentation, Compression encoding,
Sparse representation, Multi-scale attention fusion
BibRef
Wang, S.Q.[Si-Qi],
Wu, H.[Hao],
Yu, X.S.[Xiao-Sheng],
Wu, C.D.[Cheng-Dong],
Unsupervised Domain Adaptive Medical Segmentation Network Based on
Contrastive Learning,
IJIST(35), No. 6, 2025, pp. e70210.
DOI Link Code:
WWW Link.
2510
contrastive learning, medical segmentation, unsupervised domain adaptation
BibRef
Zhao, J.W.[Jian-Wei],
Yang, F.[Fan],
Li, X.[Xin],
Jiao, Z.C.[Zhi-Cheng],
Zhai, Q.[Qiang],
Li, X.M.[Xiao-Meng],
Wu, D.[De],
Fu, H.Z.[Hua-Zhu],
Cheng, H.[Hong],
SegMIC: A universal model for medical image segmentation through
in-context learning,
PR(171), 2026, pp. 112179.
Elsevier DOI Code:
WWW Link.
2510
Universal medical image segmentation, Multi-modality, In-context learning
BibRef
Zhang, J.[Juan],
Jiang, G.Q.[Gao-Qiang],
Li, Z.W.[Zhong-Wen],
Tian, B.[Bihan],
Yu, S.[Shuchen],
Yu, Q.X.[Qing-Xiang],
Zhou, J.[Jie],
Chen, H.[Hao],
Pu, J.T.[Jian-Tao],
Yi, Q.Y.[Quan-Yong],
Wang, L.[Lei],
Medical image segmentation using dual-decoder mutual teaching with a
mean teacher framework,
PR(171), 2026, pp. 112184.
Elsevier DOI Code:
WWW Link.
2510
Image segmentation, Semi-supervised learning,
Exponential moving average, Mutual teaching, Mean teacher
BibRef
Yang, Y.K.[Ya-Kun],
Liu, X.P.[Xiang-Ping],
Xue, H.C.[Hong-Cheng],
Feng, C.[Chungang],
Qu, H.[Hao],
Wang, L.[Longhe],
Li, L.[Lin],
CCPNet: Joining the pooling transformer and target context for
medical image segmentation,
PR(171), 2026, pp. 112139.
Elsevier DOI Code:
WWW Link.
2510
Medical image segmentation,
Pooling transformer, Target context, Tibial dyschondroplasia analysis
BibRef
Diao, Z.S.[Zhao-Shuo],
Zhang, Y.[Yan],
Yuan, Y.[Ye],
Wang, Q.[Qian],
Zhang, Y.[Ying],
Gao, Y.[Yue],
Mitigating label noise impact: A plug-and-play RECIST-based iterative
label refinement teacher-student learning paradigm for medical image
segmentation,
PR(171), 2026, pp. 112155.
Elsevier DOI
2510
Biomedical segmentation, Label noise, Teacher-student,
Convolutional neural networks
BibRef
Song, J.C.[Jin-Cai],
Chen, H.P.[Hai-Peng],
Lyu, Y.[Yingda],
Nie, W.Z.[Wei-Zhi],
Liu, A.A.[An-An],
Causality-Inspired Unsupervised Domain Adaptation With Target Style
Imitation for Medical Image Segmentation,
CirSysVideo(35), No. 10, October 2025, pp. 10175-10187.
IEEE DOI
2510
Image segmentation, Biomedical imaging, Training,
Adaptation models, Testing, Training data, Semantics, data augmentation
BibRef
Li, G.[Guorun],
Liu, L.[Lei],
Du, Y.F.[Yue-Feng],
Liu, P.[Peng],
Li, X.Y.[Xiao-Yu],
Qi, T.[Taiguo],
Qiao, Z.[Zhi],
Chen, D.[Du],
Song, Z.[Zhenghe],
A recurrent-attention mechanism for medical image segmentation,
PR(171), 2026, pp. 112252.
Elsevier DOI
2511
Medical image segmentation, Attention mechanism,
Skip connection, Self-attention, Recurrent-attention
BibRef
Nguyen, V.Q.[Van Quang],
Tran, T.T.[Thi-Thao],
Truong, G.B.[Gia-Bao],
Than, N.L.[Nhu-Linh],
Hoang, N.K.[Ngoc-Khai],
Nguyen, D.H.[Dinh-Hieu],
Pham, V.T.[Van-Truong],
A dense attention Mamba-based network with Adaptive Sigmoid
Fowlkes-Mallows Loss for enhanced medical image segmentation,
IVC(163), 2025, pp. 105778.
Elsevier DOI Code:
WWW Link.
2511
Medical image segmentation, Mamba for vision, Channel-spatial attention gate,
Axial residual block, Adaptive Sigmoid Fowlkes-Mallows Loss
BibRef
Yu, C.J.[Cheng-Jin],
Zhang, H.[Hao],
Pu, C.L.[Cai-Ling],
Lv, S.Y.[Sang-Yin],
Yu, J.[Jing],
Wu, X.R.[Xiao-Rui],
Ruan, D.S.[Dong-Sheng],
Xuan, H.Y.[Han-Yu],
Yan, Y.[Yuanting],
GraphMamba: Graph-driven spatial order-aware Mamba for medical image
segmentation,
PR(171), 2026, pp. 112231.
Elsevier DOI
2511
Mamba, Graph, Medical image segmentation, Scanning strategies,
Spatial consistency
BibRef
Zhang, Y.T.[Yu-Tong],
Wang, Y.F.[Yue-Fei],
Wan, Y.X.[Yu-Xuan],
Zhao, Q.[Qinyu],
Zhao, L.Y.[Liang-Yan],
Li, B.X.[Bin-Xiong],
Zhang, L.[Li],
Chen, Z.X.[Zhi-Xuan],
A carving hierarchical information integration network for medical
image segmentation,
PR(171), 2026, pp. 112291.
Elsevier DOI Code:
WWW Link.
2511
Medical image segmentation, Carving, Regional attention, Multiple datasets
BibRef
Liu, J.[Jiarun],
Yang, H.[Hao],
Zhou, H.Y.[Hong-Yu],
Yu, L.Q.[Le-Quan],
Liang, Y.[Yong],
Yu, Y.Z.[Yi-Zhou],
Zhang, S.T.[Shao-Ting],
Zheng, H.R.[Hai-Rong],
Wang, S.S.[Shan-Shan],
Swin-UMamba†: Adapting Mamba-Based Vision Foundation Models for
Medical Image Segmentation,
MedImg(44), No. 10, October 2025, pp. 3898-3908.
IEEE DOI Code:
WWW Link.
2511
Image segmentation, Data models, Transformers,
Medical diagnostic imaging, Adaptation models,
foundation model adaption
BibRef
Nordström, M.[Marcus],
Maki, A.[Atsuto],
Hult, H.[Henrik],
The Impact Label Noise and Choice of Threshold has on Cross-Entropy
and Soft-Dice in Image Segmentation,
CVPR25(20820-20829)
IEEE DOI
2508
Medical image segmentation.
Measurement, Image segmentation, Solid modeling,
Computational modeling, Noise, Stability analysis, medical imaging
BibRef
Qiu, K.P.[Kun-Peng],
Gao, Z.Q.[Zhi-Qiang],
Zhou, Z.Y.[Zhi-Ying],
Sun, M.J.[Ming-Jie],
Guo, Y.X.[Yong-Xin],
Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and
Segmentation,
CVPR25(15672-15681)
IEEE DOI
2508
Training, Deep learning, Image segmentation, Image synthesis,
Scalability, Noise, Diffusion models, Robustness, Biomedical imaging,
medical image segmentation
BibRef
Rahman, M.M.[Md Motiur],
Trabelsi, M.[Mohamed],
Uzunalioglu, H.[Huseyin],
Boyd, A.[Aidan],
Personalized Mixture of Experts for Multi-Site Medical Image
Segmentation,
WACV25(3172-3184)
IEEE DOI
2505
Image segmentation, Data privacy, Federated learning, Predictive models,
Data models, Regulation, Synchronization, Servers, Biomedical imaging
BibRef
Karimijarbigloo, S.[Sanaz],
Kolahi, S.G.[Sina Ghorbani],
Azad, R.[Reza],
Bagci, U.[Ulas],
Merhof, D.[Dorit],
Frequency-Domain Refinement of Vision Transformers for Robust Medical
Image Segmentation Under Degradation,
WACV25(9176-9185)
IEEE DOI
2505
Degradation, Wavelet transforms, Image segmentation,
Frequency-domain analysis, Noise, Contrastive learning.
BibRef
Xie, B.[Bin],
Tang, H.[Hao],
Cai, D.[Dawen],
Yan, Y.[Yan],
MS-UMLP: Medical Image Segmentation via Multi-scale U-shape Mlp-mixer,
ACCV24(X: 325-341).
Springer DOI
2412
BibRef
Tian, Y.[Yu],
Wen, C.C.[Cong-Cong],
Shi, M.[Min],
Afzal, M.M.[Muhammad Muneeb],
Huang, H.[Hao],
Khan, M.O.[Muhammad Osama],
Luo, Y.[Yan],
Fang, Y.[Yi],
Wang, M.Y.[Meng-Yu],
Fairdomain: Achieving Fairness in Cross-domain Medical Image
Segmentation and Classification,
ECCV24(LXXVI: 251-271).
Springer DOI
2412
BibRef
Xu, C.[Chen],
Huang, Q.M.[Qi-Ming],
Hou, Y.Q.[Yu-Qi],
Wu, J.X.[Jiang-Xing],
Zhang, F.[Fan],
Chang, H.J.[Hyung Jin],
Jiao, J.B.[Jian-Bo],
Few Exemplar-based General Medical Image Segmentation via Domain-aware
Selective Adaptation,
ACCV24(II: 159-173).
Springer DOI
2412
BibRef
Khan, T.M.[Tariq M],
Iqbal, S.[Shahzaib],
Naqvi, S.S.[Syed S.],
Razzak, I.[Imran],
Meijering, E.[Erik],
LMBF-Net: A Lightweight Multipath Bidirectional Focal Attention
Network for Multifeatures Segmentation,
ICIP24(2807-2813)
IEEE DOI
2411
Optical filters, Integrated optics, Image segmentation,
Convolution, Modulation, Optical fiber networks, Optical imaging,
Medical Image Segmentation
BibRef
Du, Q.Y.[Qian-Yu],
Zhong, B.J.[Bao-Jiang],
Ma, K.K.[Kai-Kuang],
ATU-NET: An Adaptive Transformation-Based U-NET for Medical Image
Segmentation,
ICIP24(2989-2995)
IEEE DOI
2411
Training, Image segmentation, Adaptive systems, Transforms,
Network architecture, Benchmark testing, Feature extraction,
encoder-decoder architecture
BibRef
Wu, J.[Junde],
Xu, M.[Min],
One-Prompt to Segment All Medical Images,
CVPR24(11302-11312)
IEEE DOI Code:
WWW Link.
2410
Learning systems, Image segmentation, Adaptation models,
Visualization, Costs, Computational modeling
BibRef
Perera, S.[Shehan],
Navard, P.[Pouyan],
Yilmaz, A.[Alper],
SegFormer3D: an Efficient Transformer for 3D Medical Image
Segmentation,
DEF-AI-MIA24(4981-4988)
IEEE DOI Code:
WWW Link.
2410
Training, Image segmentation, Solid modeling, Computational modeling,
Transformers, Deep Learning
BibRef
Ding, Y.H.[Yu-Hang],
Li, L.[Liulei],
Wang, W.G.[Wen-Guan],
Yang, Y.[Yi],
Clustering Propagation for Universal Medical Image Segmentation,
CVPR24(3357-3369)
IEEE DOI
2410
Training, Knowledge engineering, Image segmentation,
Solid modeling, Image coding
BibRef
Cheng, Z.H.[Zhi-Heng],
Wei, Q.Y.[Qing-Yue],
Zhu, H.[Hongru],
Wang, Y.[Yan],
Qu, L.Q.[Liang-Qiong],
Shao, W.[Wei],
Zhou, Y.[Yuyin],
Unleashing the Potential of SAM for Medical Adaptation via
Hierarchical Decoding,
CVPR24(3511-3522)
IEEE DOI Code:
WWW Link.
2410
Training, Image segmentation, Adaptation models, Costs,
Training data, Probabilistic logic, Data models,
medical image segmentation
BibRef
Rakic, M.[Marianne],
Wong, H.E.[Hallee E.],
Ortiz, J.J.G.[Jose Javier Gonzalez],
Cimini, B.A.[Beth A.],
Guttag, J.V.[John V.],
Dalca, A.V.[Adrian V.],
Tyche: Stochastic in-Context Learning for Medical Image Segmentation,
CVPR24(11159-11173)
IEEE DOI Code:
WWW Link.
2410
Training, Learning systems, Image segmentation, Uncertainty,
Convolution, Stochastic processes, Machine learning, uncertainty,
medical imaging
BibRef
Chen, Z.Y.[Zi-Yang],
Pan, Y.S.[Yong-Sheng],
Ye, Y.W.[Yi-Wen],
Lu, M.K.[Meng-Kang],
Xia, Y.[Yong],
Each Test Image Deserves A Specific Prompt: Continual Test-Time
Adaptation for 2D Medical Image Segmentation,
CVPR24(11184-11193)
IEEE DOI Code:
WWW Link.
2410
Training, Adaptation models, Visualization, Codes,
Semantic segmentation, Benchmark testing
BibRef
Wu, Y.C.[Yi-Cheng],
Luo, X.[Xiangde],
Xu, Z.[Zhe],
Guo, X.Q.[Xiao-Qing],
Ju, L.[Lie],
Ge, Z.Y.[Zong-Yuan],
Liao, W.J.[Wen-Jun],
Cai, J.F.[Jian-Fei],
Diversified and Personalized Multi-Rater Medical Image Segmentation,
CVPR24(11470-11479)
IEEE DOI Code:
WWW Link.
2410
Training, Image segmentation, Adaptation models, Codes, Uncertainty,
Annotations, Computational modeling
BibRef
Chen, X.Y.[Xiao-Yang],
Zheng, H.[Hao],
Li, Y.M.[Yue-Meng],
Ma, Y.C.[Yun-Cong],
Ma, L.[Liang],
Li, H.M.[Hong-Ming],
Fan, Y.[Yong],
Versatile Medical Image Segmentation Learned from Multi-Source
Datasets via Model Self-Disambiguation,
CVPR24(11747-11756)
IEEE DOI
2410
Training, Image segmentation, Solid modeling, Protocols, Annotations,
Prevention and mitigation, Computational modeling
BibRef
Dong, H.Y.[Hao-Yu],
Konz, N.[Nicholas],
Gu, H.[Hanxue],
Mazurowski, M.A.[Maciej A.],
Medical Image Segmentation with InTEnt: Integrated Entropy Weighting
for Single Image Test-Time Adaptation,
DEF-AI-MIA24(5046-5055)
IEEE DOI
2410
Training, Image segmentation, Adaptation models, Estimation,
Predictive models, Nonhomogeneous media, Entropy,
test-time adaptation
BibRef
Aleem, S.[Sidra],
Wang, F.[Fangyijie],
Maniparambil, M.[Mayug],
Arazo, E.[Eric],
Dietlmeier, J.[Julia],
Curran, K.[Kathleen],
O'Connor, N.E.[Noel E.],
Little, S.[Suzanne],
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for
Zero-shot Medical Image Segmentation,
DEF-AI-MIA24(5184-5193)
IEEE DOI
2410
Image segmentation, Adaptation models, Filtering,
Inference mechanisms, Lung, Robustness, Pattern recognition
BibRef
Nam, J.H.[Ju-Hyeon],
Syazwany, N.S.[Nur Suriza],
Kim, S.J.[Su Jung],
Lee, S.C.[Sang-Chul],
Modality-Agnostic Domain Generalizable Medical Image Segmentation by
Multi-Frequency in Multi-Scale Attention,
CVPR24(11480-11491)
IEEE DOI
2410
Image segmentation, Image analysis, Frequency-domain analysis,
Feature extraction, Data mining, Noise measurement, Image Segmentation
BibRef
Schmidt-Mengin, M.[Marius],
Benichoux, A.[Alexis],
Belachew, S.[Shibeshih],
Komodakis, N.[Nikos],
Paragios, N.[Nikos],
ToNNO: Tomographic Reconstruction of a Neural Network's Output for
Weakly Supervised Segmentation of 3D Medical Images,
CVPR24(11428-11438)
IEEE DOI
2410
Training, Solid modeling, Semantic segmentation, Transforms,
Tomography, Pattern recognition
BibRef
Li, Z.[Zhe],
Yang, L.T.[Laurence T.],
Ren, B.[Bocheng],
Nie, X.[Xin],
Gao, Z.Y.[Zhang-Yang],
Tan, C.[Cheng],
Li, S.Z.[Stan Z.],
MLIP: Enhancing Medical Visual Representation with Divergence Encoder
and Knowledge-guided Contrastive Learning,
CVPR24(11704-11714)
IEEE DOI Code:
WWW Link.
2410
Representation learning, Visualization, Semantic segmentation,
Contrastive learning, Object detection, Pattern recognition
BibRef
Azad, R.[Reza],
Niggemeier, L.[Leon],
Hüttemann, M.[Michael],
Kazerouni, A.[Amirhossein],
Aghdam, E.K.[Ehsan Khodapanah],
Velichko, Y.[Yury],
Bagci, U.[Ulas],
Merhof, D.[Dorit],
Beyond Self-Attention: Deformable Large Kernel Attention for Medical
Image Segmentation,
WACV24(1276-1286)
IEEE DOI
2404
Image segmentation, Adaptation models, Convolution,
Computational modeling, Transformers, Data models, Algorithms,
Biomedical / healthcare / medicine
BibRef
Rahman, M.M.[Md Motiur],
Shokouhmand, S.[Shiva],
Bhatt, S.[Smriti],
Faezipour, M.[Miad],
MIST: Medical Image Segmentation Transformer with Convolutional
Attention Mixing (CAM) Decoder,
WACV24(403-412)
IEEE DOI
2404
Convolutional codes, Image segmentation, Computational modeling,
Semantics, Transformers, Decoding, Kernel, Algorithms,
Biomedical / healthcare / medicine
BibRef
Leng, T.[Tianang],
Zhang, Y.M.[Yi-Ming],
Han, K.[Kun],
Xie, X.H.[Xiao-Hui],
Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation
with Meta-Learning,
WACV24(7910-7920)
IEEE DOI Code:
WWW Link.
2404
Training, Metalearning, Image segmentation, Adaptation models,
Visualization, Technological innovation, Training data
BibRef
Gorade, V.[Vandan],
Mittal, S.[Sparsh],
Jha, D.[Debesh],
Bagci, U.[Ulas],
SynergyNet: Bridging the Gap between Discrete and Continuous
Representations for Precise Medical Image Segmentation,
WACV24(7753-7762)
IEEE DOI
2404
Deep learning, Image segmentation, Analytical models,
Image analysis, Skin, Applications,
Image recognition and understanding
BibRef
Schmidt, A.[Arne],
Morales-Álvarez, P.[Pablo],
Molina, R.[Rafael],
Probabilistic Modeling of Inter- and Intra-observer Variability in
Medical Image Segmentation,
ICCV23(21040-21049)
IEEE DOI
2401
BibRef
Singh, P.[Pranav],
Chen, L.[Luoyao],
Chen, M.[Mei],
Pan, J.Q.[Jin-Qian],
Chukkapalli, R.[Raviteja],
Chaudhari, S.[Shravan],
Cirrone, J.[Jacopo],
Enhancing Medical Image Segmentation: Optimizing Cross-Entropy
Weights and Post-Processing with Autoencoders,
CVAMD23(2676-2685)
IEEE DOI
2401
BibRef
Pandey, S.[Sumit],
Chen, K.F.[Kuan-Fu],
Dam, E.B.[Erik B.],
Comprehensive Multimodal Segmentation in Medical Imaging:
Combining YOLOv8 with SAM and HQ-SAM Models,
CVAMD23(2584-2590)
IEEE DOI
2401
BibRef
Pal, D.[Debojyoti],
Meena, T.[Tanushree],
Mahapatra, D.[Dwarikanath],
Roy, S.[Sudipta],
AW-Net: A Novel Fully Connected Attention-based Medical Image
Segmentation Model,
CVAMD23(2524-2533)
IEEE DOI Code:
WWW Link.
2401
BibRef
Bastico, M.[Matteo],
Ryckelynck, D.[David],
Corté, L.[Laurent],
Tillier, Y.[Yannick],
Decencière, E.[Etienne],
A Simple and Robust Framework for Cross-Modality Medical Image
Segmentation applied to Vision Transformers,
LXCV-ICCV23(4130-4140)
IEEE DOI Code:
WWW Link.
2401
BibRef
Butoi, V.I.[Victor Ion],
Ortiz, J.J.G.[Jose Javier Gonzalez],
Ma, T.Y.[Tian-Yu],
Sabuncu, M.R.[Mert R.],
Guttag, J.[John],
Dalca, A.V.[Adrian V.],
UniverSeg: Universal Medical Image Segmentation,
ICCV23(21381-21394)
IEEE DOI Code:
WWW Link.
2401
BibRef
Marinov, Z.[Zdravko],
Reiß, S.[Simon],
Kersting, D.[David],
Kleesiek, J.[Jens],
Stiefelhagen, R.[Rainer],
Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning
for Semantic Segmentation in Medical Imaging,
CVAMD23(2275-2285)
IEEE DOI Code:
WWW Link.
2401
BibRef
Hu, H.[Haigen],
Jin, Z.C.[Zhi-Chao],
Zhou, Q.W.[Qian-Wei],
Guan, Q.[Qiu],
Chen, Q.[Qi],
CTI-Unet: Hybrid Local Features and Global Representations
Efficiently,
ICIP23(735-739)
IEEE DOI Code:
WWW Link.
2312
BibRef
Li, J.Q.[Jiu-Qiang],
MCTE: Marrying Convolution and Transformer Efficiently for End-to-End
Medical Image Segmentation,
ICIP23(1100-1104)
IEEE DOI
2312
BibRef
Huang, Y.[Yao],
Liu, J.M.[Jian-Ming],
Chen, H.[Hua],
Self-Reinforcing For Few-Shot Medical Image Segmentation,
ICIP23(655-659)
IEEE DOI Code:
WWW Link.
2312
BibRef
Jiang, M.R.[Mei-Rui],
Roth, H.R.[Holger R.],
Li, W.Q.[Wen-Qi],
Yang, D.[Dong],
Zhao, C.[Can],
Nath, V.[Vishwesh],
Xu, D.G.[Da-Guang],
Dou, Q.[Qi],
Xu, Z.Y.[Zi-Yue],
Fair Federated Medical Image Segmentation via Client Contribution
Estimation,
CVPR23(16302-16311)
IEEE DOI
2309
BibRef
Rahman, A.[Aimon],
Valanarasu, J.M.J.[Jeya Maria Jose],
Hacihaliloglu, I.[Ilker],
Patel, V.M.[Vishal M.],
Ambiguous Medical Image Segmentation Using Diffusion Models,
CVPR23(11536-11546)
IEEE DOI
2309
BibRef
Santhirasekaram, A.[Ainkaran],
Winkler, M.[Mathias],
Rockall, A.[Andrea],
Glocker, B.[Ben],
Topology Preserving Compositionality for Robust Medical Image
Segmentation,
TAG-PRA23(543-552)
IEEE DOI
2309
BibRef
Yuan, M.Z.[Ming-Ze],
Xia, Y.[Yingda],
Dong, H.X.[He-Xin],
Chen, Z.[Zifan],
Yao, J.[Jiawen],
Qiu, M.Y.[Ming-Yan],
Yan, K.[Ke],
Yin, X.L.[Xiao-Li],
Shi, Y.[Yu],
Chen, X.[Xin],
Liu, Z.[Zaiyi],
Dong, B.[Bin],
Zhou, J.[Jingren],
Lu, L.[Le],
Zhang, L.[Ling],
Zhang, L.[Li],
Devil is in the Queries: Advancing Mask Transformers for Real-world
Medical Image Segmentation and Out-of-Distribution Localization,
CVPR23(23879-23889)
IEEE DOI
2309
BibRef
Jeong, S.W.[Seung-Wan],
Cho, H.H.[Hwan-Ho],
Kwon, J.[Junmo],
Park, H.[Hyunjin],
Region-of-interest Attentive Heteromodal Variational Encoder-decoder
for Segmentation with Missing Modalities,
ACCV22(VI:132-148).
Springer DOI
2307
BibRef
Zheng, Z.[Zhou],
Hayashi, Y.[Yuichiro],
Oda, M.[Masahiro],
Kitasaka, T.[Takayuki],
Mori, K.[Kensaku],
Trimix: A General Framework for Medical Image Segmentation from Limited
Supervision,
ACCV22(VI:185-202).
Springer DOI
2307
BibRef
Tian, M.[Mu],
Yang, Q.[Qinzhu],
Gao, Y.[Yi],
Multi-scale Multi-task Distillation for Incremental 3d Medical Image
Segmentation,
MCV22(369-384).
Springer DOI
2304
BibRef
Salpea, N.[Natalia],
Tzouveli, P.[Paraskevi],
Kollias, D.[Dimitrios],
Medical Image Segmentation: A Review of Modern Architectures,
MIA-COVID19D22(691-708).
Springer DOI
2304
BibRef
Tragakis, A.[Athanasios],
Kaul, C.[Chaitanya],
Murray-Smith, R.[Roderick],
Husmeier, D.[Dirk],
The Fully Convolutional Transformer for Medical Image Segmentation,
WACV23(3649-3658)
IEEE DOI
2302
Convolutional codes, Image segmentation,
Technological innovation, Semantic segmentation, Semantics,
Applications: Biomedical/healthcare/medicine
BibRef
Heidari, M.[Moein],
Kazerouni, A.[Amirhossein],
Soltany, M.[Milad],
Azad, R.[Reza],
Aghdam, E.K.[Ehsan Khodapanah],
Cohen-Adad, J.[Julien],
Merhof, D.[Dorit],
HiFormer: Hierarchical Multi-scale Representations Using Transformers
for Medical Image Segmentation,
WACV23(6191-6201)
IEEE DOI
2302
Image segmentation, Correlation, Convolution,
Computational modeling, Transformers, Biomedical/healthcare/medicine
BibRef
Rahman, M.M.[Md Mostafijur],
Munir, M.[Mustafa],
Marculescu, R.[Radu],
EMCAD: Efficient Multi-Scale Convolutional Attention Decoding for
Medical Image Segmentation,
CVPR24(11769-11779)
IEEE DOI Code:
WWW Link.
2410
Image segmentation, Convolution, Semantic segmentation,
Point of care, Logic gates, Decoding, Computational efficiency,
Medical Image Segmentation
BibRef
Rahman, M.M.[Md Mostafijur],
Marculescu, R.[Radu],
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D
Medical Image Segmentation,
WACV24(7713-7722)
IEEE DOI Code:
WWW Link.
2404
BibRef
Earlier:
Medical Image Segmentation via Cascaded Attention Decoding,
WACV23(6211-6220)
IEEE DOI
2302
Image segmentation, Convolution, Semantic segmentation, Semantics,
Transformers, Skin, Decoding, Applications, and algorithms.
Medical services, Logic gates, Lesions: Biomedical/healthcare/medicine
BibRef
Cho, W.W.[Won-Woo],
Park, J.[Jeonghoon],
Choo, J.[Jaegul],
Training Auxiliary Prototypical Classifiers for Explainable Anomaly
Detection in Medical Image Segmentation,
WACV23(2623-2632)
IEEE DOI
2302
Training, Image segmentation, Machine learning algorithms,
Pipelines, Training data, Network architecture, Data processing,
visual reasoning
BibRef
Guo, P.F.[Peng-Fei],
Yang, D.[Dong],
Hatamizadeh, A.[Ali],
Xu, A.[An],
Xu, Z.Y.[Zi-Yue],
Li, W.Q.[Wen-Qi],
Zhao, C.[Can],
Xu, D.[Daguang],
Harmon, S.[Stephanie],
Turkbey, E.[Evrim],
Turkbey, B.[Baris],
Wood, B.[Bradford],
Patella, F.[Francesca],
Stellato, E.[Elvira],
Carrafiello, G.[Gianpaolo],
Patel, V.M.[Vishal M.],
Roth, H.R.[Holger R.],
Auto-FedRL: Federated Hyperparameter Optimization for
Multi-institutional Medical Image Segmentation,
ECCV22(XXI:437-455).
Springer DOI
2211
BibRef
Shen, H.R.[Hao-Ran],
Zhang, Y.[Yifu],
Wang, W.X.[Wen-Xuan],
Chen, C.[Chen],
Liu, J.[Jing],
Song, S.S.[Shan-Shan],
Li, J.[Jiangyun],
Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical
Volumetric Segmentation,
WACV24(7856-7866)
IEEE DOI Code:
WWW Link.
2404
Training, Adaptation models, Image segmentation,
Quantization (signal), Magnetic resonance imaging, Pipelines,
Image recognition and understanding
BibRef
Wang, W.X.[Wen-Xuan],
Chen, C.[Chen],
Wang, J.[Jing],
Zha, S.[Sen],
Zhang, Y.[Yan],
Li, J.Y.[Jiang-Yun],
Med-DANet: Dynamic Architecture Network for Efficient Medical
Volumetric Segmentation,
ECCV22(XXI:506-522).
Springer DOI
2211
BibRef
Zhou, Z.Q.[Zi-Qi],
Qi, L.[Lei],
Shi, Y.H.[Ying-Huan],
Generalizable Medical Image Segmentation via Random Amplitude Mixup and
Domain-Specific Image Restoration,
ECCV22(XXI:420-436).
Springer DOI
2211
BibRef
Gupta, S.[Saumya],
Hu, X.L.[Xiao-Ling],
Kaan, J.[James],
Jin, M.[Michael],
Mpoy, M.[Mutshipay],
Chung, K.[Katherine],
Singh, G.[Gagandeep],
Saltz, M.[Mary],
Kurc, T.[Tahsin],
Saltz, J.[Joel],
Tassiopoulos, A.[Apostolos],
Prasanna, P.[Prateek],
Chen, C.[Chao],
Learning Topological Interactions for Multi-Class Medical Image
Segmentation,
ECCV22(XXIX:701-718).
Springer DOI
2211
BibRef
Liu, L.[Libo],
Fan, X.X.[Xin-Xin],
Zhang, X.D.[Xiao-Dong],
Hu, Q.M.[Qing-Mao],
Lightweight Dual-Domain Network for Real-Time Medical Image
Segmentation,
ICIP22(396-400)
IEEE DOI
2211
Image segmentation, Convolution, Frequency-domain analysis,
Semantics, Surgery, Stroke (medical condition), Real-time systems,
lightweight dual-domain network
BibRef
Cheng, J.L.[Jun-Long],
Gao, C.R.[Cheng-Rui],
Li, C.L.[Chang-Lin],
Ming, Z.Q.[Zhang-Qiang],
Yang, Y.[Yong],
Wang, F.J.[Feng-Jie],
Zhu, M.[Min],
F2RNET: A Full-Resolution Representation Network for Biomedical Image
Segmentation,
ICIP22(2406-2410)
IEEE DOI
2211
Deep learning, Image segmentation, Image resolution, Convolution,
Multilayer perceptrons, Feature extraction, Transformers,
Biomedical image segmentation
BibRef
Khan, T.M.[Tariq M.],
Robles-Kelly, A.[Antonio],
Naqvi, S.S.[Syed S.],
T-Net: A Resource-Constrained Tiny Convolutional Neural Network for
Medical Image Segmentation,
WACV22(1799-1808)
IEEE DOI
2202
Image segmentation, Skin, Retinal vessels,
Mobile handsets, Lesions, Convolutional neural networks, Image Processing
BibRef
Hatamizadeh, A.[Ali],
Tang, Y.C.[Yu-Cheng],
Nath, V.[Vishwesh],
Yang, D.[Dong],
Myronenko, A.[Andriy],
Landman, B.[Bennett],
Roth, H.R.[Holger R.],
Xu, D.G.[Da-Guang],
UNETR: Transformers for 3D Medical Image Segmentation,
WACV22(1748-1758)
IEEE DOI
2202
Image segmentation, Semantics,
Transformers, Natural language processing,
Medical Imaging/Imaging for Bioinformatics/Biological and Cell Microscopy
BibRef
Shi, D.C.[Da-Chuan],
Liu, R.Y.[Rui-Yang],
Tao, L.M.[Lin-Mi],
He, Z.X.[Zuo-Xiang],
Huo, L.[Li],
Multi-Encoder Parse-Decoder Network for Sequential Medical Image
Segmentation,
ICIP21(31-35)
IEEE DOI
2201
Training, Manifolds, Image segmentation, Neural networks,
Feature extraction, Decoding, Data mining,
Convolutional neural networks
BibRef
Koker, T.[Teddy],
Mireshghallah, F.[Fatemehsadat],
Titcombe, T.[Tom],
Kaissis, G.[Georgios],
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation,
ICIP21(394-398)
IEEE DOI
2201
Deep learning, Image segmentation, Sensitivity,
Computed tomography, Decision making, Distance measurement, Medical Imaging
BibRef
Bhide, S.,
Mikut, R.,
Leptin, M.,
Stegmaier, J.,
Semi-Automatic Generation Of Tight Binary Masks And Non-Convex
Isosurfaces For Quantitative Analysis of 3D Biological Samples,
ICIP20(2820-2824)
IEEE DOI
2011
Image segmentation, Embryo, Shape, Isosurfaces, GUI
BibRef
Chang, Q.,
Qu, H.,
Zhang, Y.,
Sabuncu, M.,
Chen, C.,
Zhang, T.,
Metaxas, D.N.,
Synthetic Learning: Learn From Distributed Asynchronized
Discriminator GAN Without Sharing Medical Image Data,
CVPR20(13853-13863)
IEEE DOI
2008
Biomedical imaging, Generators, Data privacy,
Task analysis, Image segmentation, Data models
BibRef
Smith, T.J.[Thomas J.],
Valstar, M.[Michel],
Sharkey, D.[Don],
Crowe, J.[John],
Clinical Scene Segmentation with Tiny Datasets,
CVPM19(1637-1645)
IEEE DOI
2004
convolutional neural nets, graph theory, image representation,
image segmentation, learning (artificial intelligence), End to end
BibRef
Xu, X.,
Lu, Q.,
Yang, L.,
Hu, S.,
Chen, D.,
Hu, Y.,
Shi, Y.,
Quantization of Fully Convolutional Networks for Accurate Biomedical
Image Segmentation,
CVPR18(8300-8308)
IEEE DOI
1812
Quantization (signal), Training, Biomedical imaging,
Image segmentation, Uncertainty, Memory management, Neural networks
BibRef
Kromp, F.,
Ambros, I.,
Weiss, T.,
Bogen, D.,
Dodig, H.,
Berneder, M.,
Gerber, T.,
Taschner-Mandl, S.,
Ambros, P.,
Hanbury, A.,
Machine learning framework incorporating expert knowledge in tissue
image annotation,
ICPR16(343-348)
IEEE DOI
1705
Algorithm design and analysis, Biological tissues,
Image segmentation, Machine learning algorithms, Morphology,
Prediction algorithms, image annotation, machine learning, online, training
BibRef
Mesbah, S.,
Shalaby, A.,
Willhite, A.,
Harkema, S.,
Rejc, E.,
El-baz, A.,
Automatic 3-D muscle and fat segmentation of thigh magnetic resonance
images in individuals with spinal cord injury,
ICIP17(3280-3284)
IEEE DOI
1803
Markov processes, biomedical MRI, diseases, image registration,
image segmentation, injuries, medical disorders,
SCI
BibRef
Jarrar, M.,
Kerkeni, A.,
Abdallah, A.B.,
Bedoui, M.H.,
MLP Neural Network Classifier for Medical Image Segmentation,
CGiV16(88-93)
IEEE DOI
1608
image classification
BibRef
Zhu, H.[Hong],
Xu, J.H.[Jin-Hui],
Hu, J.F.[Jun-Feng],
Chen, J.[Jing],
Medical Image Segmentation Using Improved Affinity Propagation,
CompIMAGE16(208-215).
Springer DOI
1704
Affinity Propagation (AP) vs. Nearest Neighbor classification.
BibRef
Masci, J.[Jonathan],
Giusti, A.[Alessandro],
Ciresan, D.C.[Dan C.],
Fricout, G.[Gabriel],
Schmidhuber, J.[Jurgen],
A fast learning algorithm for image segmentation with max-pooling
convolutional networks,
ICIP13(2713-2717)
IEEE DOI
1402
Convolutional Network
BibRef
Giusti, A.[Alessandro],
Ciresan, D.C.[Dan C.],
Masci, J.[Jonathan],
Gambardella, L.M.[Luca M.],
Schmidhuber, J.[Jurgen],
Fast image scanning with deep max-pooling convolutional neural
networks,
ICIP13(4034-4038)
IEEE DOI
1402
Biomedical Imaging
BibRef
Ding, J.J.[Jian-Jiun],
Wang, Y.H.[Yu-Hsiang],
Hu, L.L.[Lee-Lin],
Chao, W.L.[Wei-Lun],
Shau, Y.W.[Yio-Wha],
Muscle injury determination by image segmentation,
VCIP11(1-4).
IEEE DOI
1201
BibRef
Kamarainen, J.K.[Joni-Kristian],
Lensu, L.[Lasse],
Kauppi, T.[Tomi],
Combining Multiple Image Segmentations by Maximizing Expert Agreement,
MLMI12(193-200).
Springer DOI
1211
BibRef
Pham, T.D.[Tuan D.],
Eisenblatter, U.[Uwe],
Golledge, J.[Jonathan],
Baune, B.T.[Bernhard T.],
Berger, K.[Klaus],
Segmentation of medical images using geo-theoretic distance matrix in
fuzzy clustering,
ICIP09(3369-3372).
IEEE DOI
0911
BibRef
Luong, H.V.[Hyunh Van],
Kim, J.M.[Jong Myon],
A New Parallel Approach to Fuzzy Clustering for Medical Image
Segmentation,
ISVC08(I: 1092-1101).
Springer DOI
0812
BibRef
Vannier, M.W.[Michael W.],
Haller, J.W.,
Biomedical image segmentation,
ICIP98(II: 20-24).
IEEE DOI
9810
BibRef
Wegner, S.,
Harms, T.,
Oswald, H.,
Fleck, E.,
The watershed transformation on graphs for the segmentation of CT
images,
ICPR96(III: 498-502).
IEEE DOI
0509
BibRef
Earlier:
Medical image segmentation using the watershed transformation on graphs,
ICIP96(III: 37-40).
IEEE DOI
9610
Image Segmentation for a Hyperthermia Planning Environment
BibRef
Wegner, S.,
Harms, T.,
Builtjes, J.H.,
Oswald, H.,
Fleck, E.,
The watershed transformation for multiresolution image segmentation,
CIAP95(31-36).
Springer DOI
9509
BibRef
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Organ Segmentation, Multi-Organ Segmentation, Anatomy Segmentation .