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Lesions, Image segmentation, Skin, Task analysis, Feature extraction,
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Lesions, Skin, Image segmentation, Feature extraction, Melanoma,
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2012
Image segmentation, Lesions, Medical diagnostic imaging, Skin,
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Lesions, Proposals,
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deep learning, dermoscopic images, multi-level preprocessing,
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2106
Dermoscopy, Digital hair removal, Skin lesion segmentation, Deep learning
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2108
geodesic, MAFCNN, Mask R-CNN, semantic segmentation, skin lesion
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Automated detection and classification of skin diseases using diverse
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2108
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Correction:
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DOI Link
2401
dataset and classification, features, morphological operation, skin disease
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Li, Y.[Yan],
Murthy, R.S.[Raksha Sreeramachandra],
Zhu, Y.[Yirui],
Zhang, F.Y.[Feng-Yi],
Tang, J.N.[Jia-Ning],
Mehrabi, J.N.[Joseph N.],
Kelly, K.M.[Kristen M.],
Chen, Z.[Zhongping],
1.7-Micron Optical Coherence Tomography Angiography for
Characterization of Skin Lesions: A Feasibility Study,
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IEEE DOI
2109
Imaging, Lesions, Visualization,
Green products, Angiography, Spatial resolution,
clinical diagnosis
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Wang, X.H.[Xiao-Hong],
Jiang, X.D.[Xu-Dong],
Ding, H.H.[Heng-Hui],
Zhao, Y.Q.[Yu-Qian],
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Knowledge-aware deep framework for collaborative skin lesion
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PR(120), 2021, pp. 108075.
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2109
Melanoma diagnosis, Knowledge-aware deep framework,
Lesion-based pooling and shape extraction, Recursive mutual learning
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Singh, L.[Lokesh],
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SLICACO: An automated novel hybrid approach for dermatoscopic
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2112
ant colony, deep learning, hybrid image segmentation, melanoma, super-pixel
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Das, S.K.[Sujit Kumar],
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Recognition of ischaemia and infection in diabetic foot ulcer:
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DOI Link
2201
convolution neural network, deep learning, diabetic foot ulcer,
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Ahmad, B.[Belal],
Usama, M.[Mohd],
Ahmad, T.[Tanvir],
Khatoon, S.[Shabnam],
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2201
bidirectional long short term memory,
convolutional neural network, skin disease classification
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Cardoen, B.[Ben],
Shokoufi, M.[Majid],
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Multitask Deep Learning Reconstruction and Localization of Lesions in
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MedImg(41), No. 3, March 2022, pp. 515-530.
IEEE DOI
2203
Image reconstruction, Optical imaging, Optical scattering,
Biomedical optical imaging, US Department of Transportation,
handheld probe
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Kundu, R.[Rohit],
Sarkar, R.[Ram],
MFSNet: A multi focus segmentation network for skin lesion
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2205
Lesion Segmentation, Deep Learning, Parallel Partial Decoder,
Attention Modules, Skin Melanoma
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Xu, M.J.[Meng-Juan],
Liu, P.[Peng],
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Shao, P.F.[Peng-Fei],
Kaffenberger, B.[Benjamin],
Xu, R.X.[Ronald X.],
Single Model Deep Learning on Imbalanced Small Datasets for Skin
Lesion Classification,
MedImg(41), No. 5, May 2022, pp. 1242-1254.
IEEE DOI
2205
Lesions, Skin, Training, Task analysis, Data models, Melanoma,
Medical diagnostic imaging, Skin lesion classification, class imbalanced
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Kaur, R.[Ranpreet],
GholamHosseini, H.[Hamid],
Sinha, R.[Roopak],
Skin lesion segmentation using an improved framework of
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IJIST(32), No. 4, 2022, pp. 1143-1158.
DOI Link
2207
deep learning, hair removal, melanoma, segmentation, skin cancer
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Singh, L.[Lokesh],
Janghel, R.R.[Rekh Ram],
Sahu, S.P.[Satya Prakash],
A hybrid feature fusion strategy for early fusion and majority voting
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IJIST(32), No. 4, 2022, pp. 1231-1250.
DOI Link
2207
feature extraction, feature fusion, majority voting, melanoma,
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Abdelazeem, R.M.[Rania M.],
Hamdy, O.[Omnia],
Utilizing the spatial frequency domain imaging to investigate change
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DOI Link
2209
hydrotherapy, skin, spatial frequency domain imaging, spatial light modulator
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Elsevier DOI
2210
Skin lesion analysis, Multi-task decoupled, Deep learning, Task causality
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Mahmoud-Ghoneim, D.[Doaa],
The effect of quantization at different resolutions on the
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IJIST(32), No. 6, 2022, pp. 1953-1962.
DOI Link
2212
automated diagnosis, cooccurrence matrix, melanoma, skin cancer,
support vector machine, texture analysis
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Wang, S.S.[Sheng-Sheng],
Dense and shuffle attention U-Net for automatic skin lesion
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DOI Link
2212
attention mechanism, deep learning, skin lesion segmentation, U-net
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Wang, S.T.[Su-Tong],
Yin, Y.Q.[Yun-Qiang],
Wang, D.J.[Du-Juan],
Wang, Y.Z.[Yan-Zhang],
Jin, Y.C.[Yao-Chu],
Interpretability-Based Multimodal Convolutional Neural Networks for
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IEEE DOI
2212
Skin, Lesions, Feature extraction, Deep learning,
Medical diagnostic imaging, Convolutional neural networks,
skin lesion diagnosis
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Zhao, X.Y.[Xiang-Yu],
Zhang, P.[Peng],
Song, F.[Fan],
Ma, C.B.[Chen-Bin],
Fan, G.D.[Guang-Da],
Sun, Y.Y.[Yang-Yang],
Feng, Y.[Youdan],
Zhang, G.L.[Guang-Lei],
Prior Attention Network for Multi-Lesion Segmentation in Medical
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MedImg(41), No. 12, December 2022, pp. 3812-3823.
IEEE DOI
2212
Image segmentation, Lesions, Feature extraction, Decoding,
Task analysis, Medical diagnostic imaging, Attention mechanism,
multi-lesion segmentation
BibRef
Al-Huda, Z.[Zaid],
Yao, Y.[Yuan],
Yao, J.[Jing],
Peng, B.[Bo],
Raza, A.[Ali],
Weakly supervised skin lesion segmentation based on spot-seeds guided
optimal regions,
IET-IPR(17), No. 1, 2023, pp. 239-255.
DOI Link
2301
BibRef
Yue, G.H.[Guang-Hui],
Wei, P.S.[Pei-Shan],
Zhou, T.W.[Tian-Wei],
Jiang, Q.P.[Qiu-Ping],
Yan, W.Q.[Wei-Qing],
Wang, T.F.[Tian-Fu],
Toward Multicenter Skin Lesion Classification Using Deep Neural
Network With Adaptively Weighted Balance Loss,
MedImg(42), No. 1, January 2023, pp. 119-131.
IEEE DOI
2301
Lesions, Skin, Task analysis, Feature extraction, Tuning,
Neural networks, Ultrasonic imaging, Skin lesion classification,
loss function
BibRef
Tajjour, S.[Salwan],
Garg, S.[Sonia],
Chandel, S.S.[Shyam Singh],
Sharma, D.[Diksha],
A novel hybrid artificial neural network technique for the early skin
cancer diagnosis using color space conversions of original images,
IJIST(33), No. 1, 2023, pp. 276-286.
DOI Link
2301
color space conversion,
convolution neural network, machine learning, skin lesions
BibRef
Yan, P.[Pu],
Wang, G.[Gang],
Chen, J.[Jie],
Tang, Q.W.[Qing-Wei],
Xu, H.[Heng],
Skin lesion classification based on the VGG-16 fusion residual
structure,
IJIST(33), No. 1, 2023, pp. 53-68.
DOI Link
2301
ISIC2018, multiclassification, ResNet, skin lesion, VGG-16
BibRef
Chen, C.Q.[Chao-Qi],
Wang, J.[Jiexiang],
Pan, J.W.[Jun-Wen],
Bian, C.[Cheng],
Zhang, Z.C.[Zhi-Cheng],
GraphSKT: Graph-Guided Structured Knowledge Transfer for Domain
Adaptive Lesion Detection,
MedImg(42), No. 2, February 2023, pp. 507-518.
IEEE DOI
2302
Lesions, Adaptation models, Task analysis, Knowledge transfer,
Feature extraction, Medical diagnostic imaging, Semantics,
intra- and inter-domain
BibRef
Liu, Z.[Zihao],
Xiong, R.Q.[Rui-Qin],
Jiang, T.T.[Ting-Ting],
CI-Net: Clinical-Inspired Network for Automated Skin Lesion
Recognition,
MedImg(42), No. 3, March 2023, pp. 619-632.
IEEE DOI
2303
Lesions, Medical services, Feature extraction, Skin,
Medical diagnostic imaging, Image segmentation, Task analysis, neural network
BibRef
Somfai, E.[Ellák],
Baffy, B.[Benjámin],
Fenech, K.[Kristian],
Hosszú, R.[Rita],
Korózs, D.[Dorina],
Pólik, M.[Marcell],
Sárdy, M.[Miklós],
Lorincz, A.[András],
Handling dataset dependence with model ensembles for skin lesion
classification from dermoscopic and clinical images,
IJIST(33), No. 2, 2023, pp. 556-571.
DOI Link
2303
deep learning, skin lesion classification
BibRef
Sun, Y.H.[Yong-Heng],
Dai, D.[Duwei],
Zhang, Q.[Qianni],
Wang, Y.Q.[Ya-Qi],
Xu, S.H.[Song-Hua],
Lian, C.F.[Chun-Feng],
MSCA-Net: Multi-scale contextual attention network for skin lesion
segmentation,
PR(139), 2023, pp. 109524.
Elsevier DOI
2304
Skin lesion segmentation, Multi-scale bridge module,
Global-local channel spatial attention module,
Scale-aware deep supervision module
BibRef
Wang, J.C.[Jia-Cheng],
Chen, F.[Fei],
Ma, Y.X.[Yu-Xi],
Wang, L.S.[Lian-Sheng],
Fei, Z.D.[Zhao-Dong],
Shuai, J.W.[Jian-Wei],
Tang, X.D.[Xiang-Dong],
Zhou, Q.C.[Qi-Chao],
Qin, J.[Jing],
XBound-Former: Toward Cross-Scale Boundary Modeling in Transformers,
MedImg(42), No. 6, June 2023, pp. 1735-1745.
IEEE DOI
2306
Lesions, Transformers, Skin, Image segmentation, Feature extraction,
Computational modeling, Task analysis, Skin lesion segmentation, cross-scale
BibRef
Khan, M.A.[Muhammad Attique],
Akram, T.[Tallha],
Zhang, Y.D.[Yu-Dong],
Alhaisoni, M.[Majed],
Hejaili, A.A.[Abdullah Al],
Shaban, K.A.[Khalid Adel],
Tariq, U.[Usman],
Zayyan, M.H.[Muhammad H.],
SkinNet-ENDO: Multiclass skin lesion recognition using deep neural
network and Entropy-Normal distribution optimization algorithm with
ELM,
IJIST(33), No. 4, 2023, pp. 1275-1292.
DOI Link
2307
classification, contrast enhancement, deep learning,
Entropy-NDOELM, hybridization, skin cancer
BibRef
Duman, E.[Erkan],
Tolan, Z.[Zafer],
Ensemble the recent architectures of deep convolutional networks for
skin diseases diagnosis,
IJIST(33), No. 4, 2023, pp. 1293-1305.
DOI Link
2307
deep Learning, dermoscopic images, ensembling CNNs, skin diseases diagnosis
BibRef
Yang, Y.Q.[Yu-Qing],
He, P.[Ping],
Wang, S.[Shengrui],
Tian, Y.[Yu],
Zhang, W.[Wei],
DB-TASNet for disease diagnosis and lesion segmentation in medical
images,
JVCIR(95), 2023, pp. 103896.
Elsevier DOI
2309
Disease diagnosis, Lesion segmentation, Transformer, U-Net, DB-TASNet
BibRef
Wang, H.[Hui],
Qi, Q.Q.[Qian-Qian],
Sun, W.J.[Wei-Jia],
Li, X.[Xue],
Dong, B.X.[Bo-Xin],
Yao, C.L.[Chun-Li],
Classification of skin lesions with generative adversarial networks
and improved MobileNetV2,
IJIST(33), No. 5, 2023, pp. 1561-1576.
DOI Link
2310
deep learning, generative adversarial networks,
medical image classification, skin lesion classification
BibRef
Jiang, Y.[Yun],
Qiao, H.[Hao],
Zhang, Z.Q.[Ze-Qun],
Wang, M.[Meiqi],
Yan, W.[Wei],
Chen, J.[Jie],
MDSC-Net: A multi-scale depthwise separable convolutional neural
network for skin lesion segmentation,
IET-IPR(17), No. 13, 2023, pp. 3713-3727.
DOI Link
2311
encoding, feature extraction, medical image processing, skin
BibRef
Chen, H.[Huai],
Wang, R.Z.[Ren-Zhen],
Wang, X.Y.[Xiu-Ying],
Li, J.[Jieyu],
Fang, Q.[Qu],
Li, H.[Hui],
Bai, J.H.[Jian-Hao],
Peng, Q.[Qing],
Meng, D.Y.[De-Yu],
Wang, L.S.[Li-Sheng],
Unsupervised Local Discrimination for Medical Images,
PAMI(45), No. 12, December 2023, pp. 15912-15929.
IEEE DOI
2311
Local features. Apply to lesions.
BibRef
Wang, Z.H.[Zhong-Hua],
Lyu, J.[Junyan],
Tang, X.Y.[Xiao-Ying],
autoSMIM: Automatic Superpixel-Based Masked Image Modeling for Skin
Lesion Segmentation,
MedImg(42), No. 12, December 2023, pp. 3501-3511.
IEEE DOI Code:
WWW Link.
2312
BibRef
Zhou, L.[Lianyu],
Yu, L.Q.[Le-Quan],
Wang, L.S.[Lian-Sheng],
RECIST-Induced Reliable Learning: Geometry-Driven Label Propagation
for Universal Lesion Segmentation,
MedImg(43), No. 1, January 2024, pp. 149-161.
IEEE DOI
2401
BibRef
Aloraini, M.[Mohammed],
An effective human monkeypox classification using vision transformer,
IJIST(34), No. 1, 2024, pp. e22944.
DOI Link
2401
deep learning, image classification, medical imaging,
monkeypox virus, vision transformer
BibRef
Zhou, Y.[Ying],
Yang, Y.[Yimei],
SRP&PASMLP-Net: Lightweight skin lesion segmentation network
based on structural re-parameterization and parallel axial shift
multilayer perceptron,
IJIST(34), No. 2, 2024, pp. e22985.
DOI Link
2402
lightweight network, parallel axial shift MLP,
skin lesion segmentation, structural re-parameterization
BibRef
Venugopal, V.[Vipin],
Nath, M.K.[Malaya Kumar],
Joseph, J.[Justin],
Das, M.V.[M. Vipin],
A deep learning-based illumination transform for devignetting
photographs of dermatological lesions,
IVC(142), 2024, pp. 104909.
Elsevier DOI
2402
Convolutional neural network, Counter exponential transform,
Deep learning, Illumination correction, Skin lesions
BibRef
Golnoori, F.[Farzad],
Boroujeni, F.Z.[Farsad Zamani],
Monadjemi, S.A.[Seyed Amirhassan],
A comparative study on deep feature selection methods for skin lesion
classification,
IET-IPR(18), No. 4, 2024, pp. 996-1013.
DOI Link
2403
feature extraction, feature selection, image classification,
medical image processing, neural nets
BibRef
Altamimi, A.[Abdulaziz],
Alrowais, F.[Fadwa],
Karamti, H.[Hanen],
Umer, M.[Muhammad],
Cascone, L.[Lucia],
Ashraf, I.[Imran],
An improved skin lesion detection solution using multi-step
preprocessing features and NASNet transfer learning model,
IVC(144), 2024, pp. 104969.
Elsevier DOI
2404
Computer vision application, Computer-aided diagnosis, Skin lesion,
Dermatology pigmented lesion classification, Transfer learning
BibRef
Du, F.[Fuhe],
Peng, B.[Bo],
Al-Huda, Z.[Zaid],
Yao, J.[Jing],
Semi-Supervised Skin Lesion Segmentation via Iterative Mask
Optimization,
IJIG(24), No. 2, March 2024, pp. 2450020.
DOI Link
2404
BibRef
He, H.L.[Hai-Long],
Paetzold, J.C.[Johannes C.],
Börner, N.[Nils],
Riedel, E.[Erik],
Gerl, S.[Stefan],
Schneider, S.[Simon],
Fisher, C.[Chiara],
Ezhov, I.[Ivan],
Shit, S.[Suprosanna],
Li, H.W.[Hong-Wei],
Rückert, D.[Daniel],
Aguirre, J.[Juan],
Biedermann, T.[Tilo],
Darsow, U.[Ulf],
Menze, B.[Bjoern],
Ntziachristos, V.[Vasilis],
Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for
Automated Extraction of Psoriasis and Aging Biomarkers,
MedImg(43), No. 6, June 2024, pp. 2074-2085.
IEEE DOI
2406
Skin, Image segmentation, Imaging, Feature extraction, Biomarkers,
Morphology, Image reconstruction, Optoacoustic mesoscopy, machine learning
BibRef
Wang, J.[Junze],
Zhang, W.J.[Wen-Jun],
Li, D.D.[Dan-Dan],
Li, C.[Chao],
Jing, W.P.[Wei-Peng],
HGSNet: A hypergraph network for subtle lesions segmentation in
medical imaging,
IET-IPR(18), No. 9, 2024, pp. 2357-2371.
DOI Link
2407
convolutional neural nets, image segmentation,
learning (artificial intelligence), medical image processing
BibRef
Hou, Q.S.[Qing-Shan],
Wang, Y.Q.[Ya-Qi],
Cao, P.[Peng],
Cheng, S.[Shuai],
Lan, L.Q.[Lin-Qi],
Yang, J.Z.[Jin-Zhu],
Liu, X.L.[Xiao-Li],
Zaiane, O.R.[Osmar R.],
A Collaborative Self-Supervised Domain Adaptation for Low-Quality
Medical Image Enhancement,
MedImg(43), No. 7, July 2024, pp. 2479-2494.
IEEE DOI
2407
Image quality, Lesions, Image enhancement, Lighting,
Anatomical structure, Task analysis, Image segmentation,
medical image quality enhancement
BibRef
Abla, R.[Rahmouni],
Abdelouahed, S.M.[Sabri My],
Abdellah, A.[Aarab],
Fine-Tuning Vision Transformers for Enhanced Skin Lesion
Classification: Navigating the Challenges of Small Datasets,
ISCV24(1-5)
IEEE DOI
2408
Training, Adaptation models, Analytical models, Accuracy,
Computational modeling, Transfer learning, Pretrained model, fine-tuning
BibRef
Gottumukkala, V.S.S.P.R.[V.S.S.P. Raju],
Kumaran, N.,
Sekhar, V.C.[V. Chandra],
IRFNet: Skin Lesion Detection and Classification Using Unified
Intuitive and Object Classifier with Iterative Random Forest Algorithm,
IJIG(24), No. 5, September 2024, pp. 2340003.
DOI Link
2410
BibRef
Yuan, F.N.[Fei-Niu],
Peng, Y.[Yuhuan],
Huang, Q.H.[Qing-Hua],
Li, X.L.[Xue-Long],
A Bi-Directionally Fused Boundary Aware Network for Skin Lesion
Segmentation,
IP(33), 2024, pp. 6340-6353.
IEEE DOI
2411
Lesions, Skin, Feature extraction, Transformers, Image segmentation,
Decoding, Convolutional neural networks, Accuracy, attention
BibRef
Wang, K.[Ke],
Chen, Z.C.[Zi-Cong],
Zhu, M.J.[Ming-Jia],
Li, Z.T.[Zhe-Tao],
Weng, J.[Jian],
Gu, T.L.[Tian-Long],
Score-Based Counterfactual Generation for Interpretable Medical Image
Classification and Lesion Localization,
MedImg(43), No. 10, October 2024, pp. 3596-3607.
IEEE DOI
2411
Lesions, Task analysis, Location awareness, Generators, Uncertainty,
Generative adversarial networks, Data models, fuzzy theory
BibRef
Zhai, G.Y.[Guang-Yao],
Wang, G.L.[Guang-Lei],
Shang, Q.H.[Qing-Hua],
Li, Y.[Yan],
Wang, H.R.[Hong-Rui],
DMA-Net: A dual branch encoder and multi-scale cross attention fusion
network for skin lesion segmentation,
IET-IPR(18), No. 14, 2024, pp. 4531-4541.
DOI Link
2501
codecs, image segmentation, skin
BibRef
Xiao, C.L.[Chun-Lun],
Zhu, A.[Anqi],
Xia, C.M.[Chun-Mei],
Qiu, Z.F.[Zi-Feng],
Liu, Y.L.[Yuan-Lin],
Zhao, C.[Cheng],
Ren, W.W.[Wei-Wei],
Wang, L.F.[Li-Fan],
Dong, L.[Lei],
Wang, T.F.[Tian-Fu],
Guo, L.H.[Le-Hang],
Lei, B.Y.[Bai-Ying],
Attention-Guided Learning With Feature Reconstruction for Skin Lesion
Diagnosis Using Clinical and Ultrasound Images,
MedImg(44), No. 1, January 2025, pp. 543-555.
IEEE DOI Code:
WWW Link.
2501
Skin, Lesions, Feature extraction, Fuses, Ultrasonic imaging,
Task analysis, Accuracy, Skin lesion classification,
clinical and ultrasound images
BibRef
Sanchez, K.[Karen],
Hinojosa, C.[Carlos],
Mieles, O.[Olinto],
Zhao, C.[Chen],
Ghanem, B.[Bernard],
Arguello, H.[Henry],
Co2Wounds-V2: Extended Chronic Wounds Dataset from Leprosy Patients,
ICIP24(69-75)
IEEE DOI
2411
Training, Visualization, Semantic segmentation, Transportation,
Medical services, Wounds, Classification algorithms, Leprosy
BibRef
Khan, U.[Ufaq],
Nawaz, U.[Umair],
Khan, M.[Mustaqeem],
Gueaieb, W.[Wail],
Saddik, A.E.[Abdulmotaleb El],
Deepskinformer: Skin Lesion Segmentation Using Hierarchical
Transformers And Edge Enhancement,
ICIP24(3868-3874)
IEEE DOI
2411
Image segmentation, Adaptation models, Shape, Image edge detection,
Benchmark testing, Transformers, Skin, Medical image processing,
DeepSkinFormer
BibRef
Wang, J.[Janet],
Zhang, Y.[Yunbei],
Ding, Z.M.[Zheng-Ming],
Hamm, J.[Jihun],
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised
Domain Adaptation,
DEF-AI-MIA24(5157-5166)
IEEE DOI
2410
Training, Protocols, Correlation, Skin,
Domain Adaptation, Skin Lesion Analysis, Fairness
BibRef
Seth, P.[Pratinav],
Pai, A.K.[Abhilash K.],
Does the Fairness of Your Pre-Training Hold Up? Examining the
Influence of Pre-Training Techniques on Skin Tone Bias in Skin Lesion
Classification,
Pretrain24(580-587)
IEEE DOI Code:
WWW Link.
2404
Training, Analytical models, Image analysis,
Computational modeling, Supervised learning,
Self-supervised learning
BibRef
Morita, T.[Takumi],
Han, X.H.[Xian-Hua],
Investigating self-supervised learning for Skin Lesion Classification,
MVA23(1-5)
DOI Link
2403
Training, Representation learning, Adaptation models,
Neural networks, Self-supervised learning, Predictive models, Skin
BibRef
Tada, M.[Masato],
Han, X.H.[Xian-Hua],
Bottleneck Transformer model with Channel Self-Attention for skin
lesion classification,
MVA23(1-5)
DOI Link
2403
Representation learning, Convolution, Network architecture,
Feature extraction, Transformers, Skin, Lesions
BibRef
Rajapaksa, S.[Sajith],
Vianney, J.M.U.[Jean Marie Uwabeza],
Castro, R.[Renell],
Khalvati, F.[Farzad],
Aich, S.[Shubhra],
Using Large Text To Image Models with Structured Prompts for Skin
Disease Identification: A Case Study,
CVAMD23(2686-2693)
IEEE DOI
2401
BibRef
Lima, G.S.[Gabriel Silva],
Pires, C.[Carolina],
Beuren, A.T.[Arlete Teresinha],
Lopes, R.P.[Rui Pedro],
Deep Learning in the Identification of Psoriatic Skin Lesions,
CIARP23(I:298-313).
Springer DOI
2312
BibRef
Barros, L.[Luana],
Chaves, L.[Levy],
Avila, S.[Sandra],
Assessing the Generalizability of Deep Neural Networks-based Models for
Black Skin Lesions,
CIARP23(II:1-14).
Springer DOI
2312
BibRef
Talavera-Martínez, L.[Lidia],
Bibiloni, P.[Pedro],
Giacaman, A.[Aniza],
Taberner, R.[Rosa],
del Pozo-Hernando, L.J.[Luis Javier],
González-Hidalgo, M.[Manuel],
Model Regularisation for Skin Lesion Symmetry Classification:
Symderm v2.0,
CAIP23(I:99-109).
Springer DOI
2312
BibRef
Xu, W.X.[Wei-Xin],
Background Masked Guided Network for Skin Lesion Segmentation in
Dermoscopy Image,
ICIP23(71-75)
IEEE DOI
2312
BibRef
Patrício, C.[Cristiano],
Neves, J.C.[João C.],
Teixeira, L.F.[Luis F.],
Coherent Concept-based Explanations in Medical Image and Its
Application to Skin Lesion Diagnosis,
SAIAD23(3799-3808)
IEEE DOI
2309
BibRef
Sheoran, M.[Manu],
Sharma, M.[Monika],
Dani, M.[Meghal],
Vig, L.[Lovekesh],
Handling Domain Shift for Lesion Detection via Semi-supervised Domain
Adaptation,
ACCVWS22(102-116).
Springer DOI
2307
BibRef
Zhao, X.[Xin],
Ren, Z.H.[Zhi-Hang],
Multi-scale Gaussian Difference Preprocessing and Dual Stream
CNN-transformer Hybrid Network for Skin Lesion Segmentation,
MMMod23(II: 671-682).
Springer DOI
2304
BibRef
Pakzad, A.[Arezou],
Abhishek, K.[Kumar],
Hamarneh, G.[Ghassan],
Circle: Color Invariant Representation Learning for Unbiased
Classification of Skin Lesions,
ISIC22(203-219).
Springer DOI
2304
BibRef
Bissoto, A.[Alceu],
Barata, C.[Catarina],
Valle, E.[Eduardo],
Avila, S.[Sandra],
Artifact-based Domain Generalization of Skin Lesion Models,
ISIC22(133-149).
Springer DOI
2304
BibRef
Chaves, L.[Levy],
Bissoto, A.[Alceu],
Valle, E.[Eduardo],
Avila, S.[Sandra],
An Evaluation of Self-supervised Pre-training for Skin-lesion Analysis,
ISIC22(150-166).
Springer DOI
2304
BibRef
Oota, S.R.[Subba Reddy],
Rowtula, V.[Vijay],
Mohammed, S.[Shahid],
Liu, M.[Minghsun],
Gupta, M.[Manish],
WSNet: Towards An Effective Method for Wound Image Segmentation,
WACV23(3233-3242)
IEEE DOI
2302
Image segmentation, Wireless sensor networks, Adaptation models,
Image analysis, Wounds, Trajectory
BibRef
Xing, L.X.[Lin-Xin],
Li, L.L.[Liang-Liang],
Wang, Z.Y.[Zhe-Yuan],
Ma, H.B.[Hong-Bing],
An Improved UNet Model for Foot Ulcer Image Segmentation,
ICIVC22(393-397)
IEEE DOI
2301
Support vector machines, Location awareness, Image segmentation,
Medical services, Wounds, Feature extraction, Diabetes, image segmentation
BibRef
Mahbod, A.[Amirreza],
Schaefer, G.[Gerald],
Ecker, R.[Rupert],
Ellinger, I.[Isabella],
Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional
Neural Networks,
ICPR22(4358-4364)
IEEE DOI
2212
Training, Image segmentation, Visualization,
Computational modeling, Wounds, Feature extraction, ensemble
BibRef
Shamsolmoali, P.[Pourya],
Zareapoor, M.[Masoumeh],
Yang, J.[Jie],
Granger, E.[Eric],
Zhou, H.Y.[Hui-Yu],
Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature
Fusion Network,
ICPR22(4219-4225)
IEEE DOI
2212
Training, Image segmentation, Convolution,
Benchmark testing, Feature extraction, Skin
BibRef
Pundhir, A.[Anshul],
Dadhich, S.[Saurabh],
Agarwal, A.[Ananya],
Raman, B.[Balasubramanian],
Towards Improved Skin Lesion Classification using Metadata
Supervision,
ICPR22(4313-4320)
IEEE DOI
2212
Manuals, Metadata, Benchmark testing, Skin,
Classification algorithms, Lesions
BibRef
Qiu, Y.[Yu],
Xu, J.[Jing],
Delving into Universal Lesion Segmentation: Method, Dataset, and
Benchmark,
ECCV22(VIII:485-503).
Springer DOI
2211
BibRef
Pewton, S.W.[Samuel William],
Yap, M.H.[Moi Hoon],
Dark Corner on Skin Lesion Image Dataset: Does it matter?,
VDU22(4830-4838)
IEEE DOI
2210
Deep learning, Measurement, Image quality, Image edge detection,
Melanoma, Skin
BibRef
Chae, H.J.[Han Joo],
Lee, S.[Seunghwan],
Son, H.[Hyewon],
Han, S.[Seungyeob],
Lim, T.[Taebin],
Generating 3D Bio-Printable Patches Using Wound Segmentation and
Reconstruction to Treat Diabetic Foot Ulcers,
CVPR22(2529-2539)
IEEE DOI
2210
Solid modeling, Image segmentation, Biological system modeling,
Computational modeling, Semantics, Surgery,
grouping and shape analysis
BibRef
Giovanetti, A.[Anita],
Canalini, L.[Laura],
Scorzoni, P.P.[Paolo Perliti],
A Compact Deep Ensemble for High Quality Skin Lesion Classification,
DeepHealth22(510-521).
Springer DOI
2208
BibRef
Cino, L.[Loris],
Mazzeo, P.L.[Pier Luigi],
Distante, C.[Cosimo],
Comparison of Different Supervised and Self-supervised Learning
Techniques in Skin Disease Classification,
CIAP22(I:77-88).
Springer DOI
2205
BibRef
Yang, D.[Dong],
Myronenko, A.[Andriy],
Wang, X.S.[Xiao-Song],
Xu, Z.Y.[Zi-Yue],
Roth, H.R.[Holger R.],
Xu, D.[Daguang],
T-AutoML: Automated Machine Learning for Lesion Segmentation using
Transformers in 3D Medical Imaging,
ICCV21(3942-3954)
IEEE DOI
2203
Training, Deep learning, Image segmentation,
Machine learning algorithms, Computer architecture, Transformers,
grouping and shape
BibRef
Kim, J.[Junho],
Kim, M.[Minsu],
Ro, Y.M.[Yong Man],
Interpretation of Lesional Detection via Counterfactual Generation,
ICIP21(96-100)
IEEE DOI
2201
Deep learning, Visualization, Image processing, Data visualization,
Medical diagnosis, Lesions, Deep learning, Explainable AI,
Medical image
BibRef
Zhou, L.[Li],
Luo, Y.[Yan],
Deep Features Fusion with Mutual Attention Transformer for Skin
Lesion Diagnosis,
ICIP21(3797-3801)
IEEE DOI
2201
Deep learning, Visualization, Image processing, Neural networks,
Skin, Lesions, skin lesion classification, attention mechanism, deep learning
BibRef
Noshad, A.[Ali],
Khonaksar, A.[Ahmadreza],
Mohebbi, M.[Mansoureh],
SkinXNet: A DoG-based Model for Automatic Detection of Skin Lesion
using Deep Learning,
IPRIA21(1-6)
IEEE DOI
2201
Visualization, Sensitivity, Malignant tumors, Dogs,
Feature extraction, Skin, Skin lesion, Melanoma,
Difference of Gaussian (DoG)
BibRef
Carvalho, R.[Rafaela],
Pedrosa, J.[João],
Nedelcu, T.[Tudor],
Multimodal Multi-tasking for Skin Lesion Classification Using Deep
Neural Networks,
ISVC21(I:27-38).
Springer DOI
2112
BibRef
Benyahia, S.[Samia],
Meftah, B.[Boudjelal],
Lézoray, O.[Olivier],
Skin Lesion Classification Using Convolutional Neural Networks Based on
Multi-Features Extraction,
CAIP21(I:466-475).
Springer DOI
2112
BibRef
Cai, J.Z.[Jin-Zheng],
Tang, Y.[Youbao],
Yan, K.[Ke],
Harrison, A.P.[Adam P.],
Xiao, J.[Jing],
Lin, G.[Gigin],
Lu, L.[Le],
Deep Lesion Tracker:
Monitoring Lesions in 4D Longitudinal Imaging Studies,
CVPR21(15154-15164)
IEEE DOI
2111
Deep learning, Databases, Imaging, Manuals, Detectors
BibRef
Mirikharaji, Z.[Zahra],
Abhishek, K.[Kumar],
Izadi, S.[Saeed],
Hamarneh, G.[Ghassan],
D-LEMA: Deep Learning Ensembles from Multiple Annotations -
Application to Skin Lesion Segmentation,
ISIC21(1837-1846)
IEEE DOI
2109
Training, Image segmentation, Uncertainty, Annotations,
Computational modeling, Training data, Predictive models
BibRef
Bissoto, A.[Alceu],
Valle, E.[Eduardo],
Avila, S.[Sandra],
GAN-Based Data Augmentation and Anonymization for Skin-Lesion
Analysis: A Critical Review,
ISIC21(1847-1856)
IEEE DOI
2109
Training, Biomedical equipment,
Medical services, Generative adversarial networks, Skin
BibRef
Mohseni, M.[Mohammadreza],
Yap, J.[Jordan],
Yolland, W.[William],
Koochek, A.[Arash],
Atkins, M.S.[M. Stella],
Can self-training identify suspicious ugly duckling lesions?,
ISIC21(1829-1836)
IEEE DOI
2109
Deep learning, Sensitivity, Malignant tumors,
Medical services, Manuals, Feature extraction
BibRef
Groh, M.[Matthew],
Harris, C.[Caleb],
Soenksen, L.[Luis],
Lau, F.[Felix],
Han, R.[Rachel],
Kim, A.[Aerin],
Koochek, A.[Arash],
Badri, O.[Omar],
Evaluating Deep Neural Networks Trained on Clinical Images in
Dermatology with the Fitzpatrick 17k Dataset,
ISIC21(1820-1828)
IEEE DOI
2109
Deep learning, Training, Image segmentation,
Dermatology, Neural networks, Training data
BibRef
Reimers, C.[Christian],
Penzel, N.[Niklas],
Bodesheim, P.[Paul],
Runge, J.[Jakob],
Denzler, J.[Joachim],
Conditional dependence tests reveal the usage of ABCD rule features
and bias variables in automatic skin lesion classification,
ISIC21(1810-1819)
IEEE DOI
2109
Training, Image color analysis, Shape, Supervised learning, Melanoma,
Skin, Classification algorithms
BibRef
Oota, S.R.[Subba Reddy],
Rowtula, V.[Vijay],
Mohammed, S.[Shahid],
Galitz, J.[Jeffrey],
Liu, M.[Minghsun],
Gupta, M.[Manish],
HealTech - A System for Predicting Patient Hospitalization Risk and
Wound Progression in Old Patients,
WACV21(2462-2471)
IEEE DOI
2106
Analytical models, Neural networks,
Evolutionary computation, Wounds, Predictive models
BibRef
Allegretti, S.[Stefano],
Bolelli, F.[Federico],
Pollastri, F.[Federico],
Longhitano, S.[Sabrina],
Pellacani, G.[Giovanni],
Grana, C.[Costantino],
Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval,
ICPR21(8053-8060)
IEEE DOI
2105
Image retrieval, Medical services, Feature extraction, Skin,
Lesions, Convolutional neural networks
BibRef
Carcagnì, P.[Pierluigi],
Leo, M.[Marco],
Celeste, G.[Giuseppe],
Distante, C.[Cosimo],
Cuna, A.[Andrea],
A Systematic Investigation on Deep Architectures for Automatic Skin
Lesions Classification,
ICPR21(8639-8646)
IEEE DOI
2105
Training, Systematics, Sociology, Pipelines, Skin, Data models, Lesions
BibRef
Gallucci, A.[Alessio],
Znamenskiy, D.[Dmitry],
Pezzotti, N.[Nicola],
Petkovic, M.[Milan],
Don't Tear Your Hair Out: Analysis of the Impact of Skin Hair on the
Diagnosis of Microscopic Skin Lesions,
AIHA20(406-416).
Springer DOI
2103
BibRef
Kumar, A.,
Hamarneh, G.,
Drew, M.S.,
Illumination-based Transformations Improve Skin Lesion Segmentation
in Dermoscopic Images,
ISIC20(3132-3141)
IEEE DOI
2008
Skin, Lesions, Image color analysis, Image segmentation, Gray-scale,
Lighting, Semantics
BibRef
Ribeiro, V.,
Avila, S.,
Valle, E.,
Less is More: Sample Selection and Label Conditioning Improve Skin
Lesion Segmentation,
ISIC20(3182-3191)
IEEE DOI
2008
Image segmentation, Lesions, Training, Machine learning, Skin,
Task analysis, Data models
BibRef
Bissoto, A.,
Valle, E.,
Avila, S.,
Debiasing Skin Lesion Datasets and Models? Not So Fast,
ISIC20(3192-3201)
IEEE DOI
2008
Lesions, Correlation, Skin, Task analysis, Training,
Feature extraction, Data models
BibRef
Barata, C.,
Santiago, C.,
How Important Is Each Dermoscopy Image?,
ISIC20(3202-3210)
IEEE DOI
2008
Training, Lesions, Skin, Task analysis, Neural networks,
Computer architecture, Image analysis
BibRef
Combalia, M.,
Hueto, F.,
Puig, S.,
Malvehy, J.,
Vilaplana, V.,
Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image
Classification,
ISIC20(3211-3220)
IEEE DOI
2008
Uncertainty, Lesions, Skin, Neural networks, Monte Carlo methods,
Training, Task analysis
BibRef
Mahajan, K.,
Sharma, M.,
Vig, L.,
Meta-DermDiagnosis: Few-Shot Skin Disease Identification using
Meta-Learning,
ISIC20(3142-3151)
IEEE DOI
2008
Skin, Diseases, Lesions, Task analysis, Training, Biomedical imaging,
Adaptation models
BibRef
Andrade, C.[Catarina],
Teixeira, L.F.[Luís F.],
Vasconcelos, M.J.M.[Maria João M.],
Rosado, L.[Luís],
Deep Learning Models for Segmentation of Mobile-acquired Dermatological
Images,
ICIAR20(II:228-237 Open Access).
Springer DOI
2007
BibRef
Bekmirzaev, S.,
Oh, S.,
Yo, S.,
RethNet: Object-by-Object Learning for Detecting Facial Skin Problems,
VRMI19(425-433)
IEEE DOI
2004
face recognition, feature extraction,
image segmentation, learning (artificial intelligence),
fine grained object categorization
BibRef
Wu, X.,
Wen, N.,
Liang, J.,
Lai, Y.,
She, D.,
Cheng, M.,
Yang, J.,
Joint Acne Image Grading and Counting via Label Distribution Learning,
ICCV19(10641-10650)
IEEE DOI
2004
Code, Dermatology.
WWW Link. diseases, learning (artificial intelligence),
medical image processing, skin, joint acne image grading, Training
BibRef
Gavrilov, D.A.,
Shchelkunov, N.N.,
Melerzanov, A.V.,
Deep Learning Based Skin Lesions Diagnosis,
PTVSBB19(81-85).
DOI Link
1912
BibRef
Tu, W.,
Liu, X.,
Hu, W.,
Pan, Z.,
Xu, X.,
Li, B.,
Segmentation of Lesion in Dermoscopy Images Using Dense-Residual
Network with Adversarial Learning,
ICIP19(1430-1434)
IEEE DOI
1910
dermoscopic image, skin lesion, convolutional neural networks,
adversarial learning, Dense-Residual block
BibRef
Wang, X.,
Ding, H.,
Jiang, X.,
Dermoscopic Image Segmentation Through the Enhanced High-Level
Parsing and Class Weighted Loss,
ICIP19(245-249)
IEEE DOI
1910
Skin lesion segmentation, fully convolutional neural network,
enhanced high-level parsing, class weighed loss
BibRef
Ferreira, B.[Bárbara],
Barata, C.[Catarina],
Marques, J.S.[Jorge S.],
What Is the Role of Annotations in the Detection of Dermoscopic
Structures?,
IbPRIA19(II:3-11).
Springer DOI
1910
BibRef
Franco-Ceballos, R.[Ricardo],
Torres-Madronero, M.C.[Maria C.],
Galeano-Zea, J.[July],
Murillo, J.[Javier],
Zarzycki, A.[Artur],
Garzon, J.[Johnson],
Robledo, S.M.[Sara M.],
Spectral Band Subset Selection for Discrimination of Healthy Skin and
Cutaneous Leishmanial Ulcers,
IbPRIA19(I:398-408).
Springer DOI
1910
BibRef
Carcagnì, P.[Pierluigi],
Leo, M.[Marco],
Cuna, A.[Andrea],
Mazzeo, P.L.[Pier Luigi],
Spagnolo, P.[Paolo],
Celeste, G.[Giuseppe],
Distante, C.[Cosimo],
Classification of Skin Lesions by Combining Multilevel Learnings in a
DenseNet Architecture,
CIAP19(I:335-344).
Springer DOI
1909
BibRef
Bonechi, S.[Simone],
Bianchini, M.[Monica],
Bongini, P.[Pietro],
Ciano, G.[Giorgio],
Giacomini, G.[Giorgia],
Rosai, R.[Riccardo],
Tognetti, L.[Linda],
Rossi, A.[Alberto],
Andreini, P.[Paolo],
Fusion of Visual and Anamnestic Data for the Classification of Skin
Lesions with Deep Learning,
NTIAP19(211-219).
Springer DOI
1909
BibRef
Piantadosi, G.[Gabriele],
Bovenzi, G.[Giampaolo],
Argenziano, G.[Giuseppe],
Moscarella, E.[Elvira],
Parmeggiani, D.[Domenico],
Docimo, L.[Ludovico],
Sansone, C.[Carlo],
Skin Lesions Classification: A Radiomics Approach with Deep CNN,
NTIAP19(252-259).
Springer DOI
1909
BibRef
Adegun, A.[Adekanmi],
Viriri, S.[Serestina],
Deep Learning Model for Skin Lesion Segmentation: Fully Convolutional
Network,
ICIAR19(II:232-242).
Springer DOI
1909
BibRef
Canalini, L.[Laura],
Pollastri, F.[Federico],
Bolelli, F.[Federico],
Cancilla, M.[Michele],
Allegretti, S.[Stefano],
Grana, C.[Costantino],
Skin Lesion Segmentation Ensemble with Diverse Training Strategies,
CAIP19(I:89-101).
Springer DOI
1909
BibRef
Czovny, R.K.,
Bellon, O.R.P.,
Silva, L.,
Costa, H.S.G.,
Minutia Matching using 3D Pore Clouds,
ICPR18(3138-3143)
IEEE DOI
1812
Dermis, Epidermis,
Databases, Biomedical imaging, Measurement uncertainty
BibRef
Yang, J.,
Sun, X.,
Liang, J.,
Rosin, P.L.,
Clinical Skin Lesion Diagnosis Using Representations Inspired by
Dermatologist Criteria,
CVPR18(1258-1266)
IEEE DOI
1812
Skin, Lesions, Diseases, Image color analysis,
Medical diagnostic imaging, Shape
BibRef
Li, H.,
He, X.,
Yu, Z.,
Zhou, F.,
Cheng, J.,
Huang, L.,
Wang, T.,
Lei, B.,
Skin Lesion Segmentation via Dense Connected Deconvolutional Network,
ICPR18(671-675)
IEEE DOI
1812
Lesions, Skin, Decoding, Training, Convolution, Imaging,
Image restoration, Skin lesion segmentation, Dermoscopy image,
Chained residual pooling
BibRef
Luo, W.,
Yang, M.,
Fast Skin Lesion Segmentation via Fully Convolutional Network with
Residual Architecture and CRF,
ICPR18(1438-1443)
IEEE DOI
1812
Lesions, Image segmentation, Convolution, Skin, Kernel, Pipelines,
Training, Melanoma, Fully Convolutional Network,
Conditional Random Field
BibRef
Filali, Y.,
Ennouni, A.,
Sabri, M.A.,
Aarab, A.,
A study of lesion skin segmentation, features selection and
classification approaches,
ISCV18(1-7)
IEEE DOI
1807
cancer, feature extraction, feature selection,
image classification, image colour analysis, image segmentation,
machine-learning
BibRef
Zeng, G.D.[Guo-Dong],
Zheng, G.[Guoyan],
Multi-scale Fully Convolutional DenseNets for Automated Skin Lesion
Segmentation in Dermoscopy Images,
ICIAR18(513-521).
Springer DOI
1807
BibRef
Mahdiraji, S.A.,
Baleghi, Y.,
Sakhaei, S.M.,
Skin lesion images classification using new color pigmented boundary
descriptors,
IPRIA17(102-107)
IEEE DOI
1712
cameras, cancer, feature extraction, image classification,
image colour analysis, image texture, medical image processing,
Skin lesion
BibRef
Rundo, F.[Francesco],
Conoci, S.[Sabrina],
Banna, G.L.[Giuseppe L.],
Stanco, F.[Filippo],
Battiato, S.[Sebastiano],
Bio-Inspired Feed-Forward System for Skin Lesion Analysis, Screening
and Follow-Up,
CIAP17(II:399-409).
Springer DOI
1711
BibRef
Balducci, F.[Fabrizio],
Grana, C.[Costantino],
Pixel Classification Methods to Detect Skin Lesions on Dermoscopic
Medical Images,
CIAP17(II:444-455).
Springer DOI
1711
BibRef
Al-abayechi, A.A.A.[Alaa Ahmed Abbas],
Jalab, H.A.[Hamid A.],
Ibrahim, R.W.[Rabha W.],
Hasan, A.M.[Ali M.],
Image Enhancement Based on Fractional Poisson for Segmentation of Skin
Lesions Using the Watershed Transform,
IVIC17(249-259).
Springer DOI
1711
BibRef
Bozkurt, A.,
Gale, T.,
Kose, K.,
Alessi-Fox, C.,
Brooks, D.H.,
Rajadhyaksha, M.,
Dy, J.G.,
Delineation of Skin Strata in Reflectance Confocal Microscopy Images
with Recurrent Convolutional Networks,
Microscopy17(777-785)
IEEE DOI
1709
Dermis, Epidermis, Feature extraction, Imaging, Training
BibRef
Hajdu, A.,
Harangi, B.,
Besenczi, R.,
Lázár, I.,
Emri, G.,
Hajdu, L.,
Tijdeman, R.,
Measuring regularity of network patterns by grid approximations using
the LLL algorithm,
ICPR16(1524-1529)
IEEE DOI
1705
Approximation algorithms, Lesions, Measurement uncertainty,
Noise level, Pigments, Skin
BibRef
Kaur, P.,
Dana, K.J.,
Cula, G.O.,
Mack, M.C.,
Hybrid deep learning for Reflectance Confocal Microscopy skin images,
ICPR16(1466-1471)
IEEE DOI
1705
Epidermis, Histograms, Image recognition, Libraries,
Machine learning, Neural, networks
BibRef
Pal, A.,
Chaturvedi, A.,
Garain, U.,
Chandra, A.,
Chatterjee, R.,
Severity grading of psoriatic plaques using deep CNN based multi-task
learning,
ICPR16(1478-1483)
IEEE DOI
1705
Computer architecture, Convolution, Diseases, Drugs, Estimation,
Kernel, Skin
BibRef
Liao, H.F.[Hao-Fu],
Li, Y.[Yuncheng],
Luo, J.B.[Jie-Bo],
Skin disease classification versus skin lesion characterization:
Achieving robust diagnosis using multi-label deep neural networks,
ICPR16(355-360)
IEEE DOI
1705
Dermatology, Diseases, Lesions, Malignant tumors, Skin, Training,
Visualization, convolutional neural networks,
skin disease classification, skin, lesion, characterization
BibRef
Jafari, M.H.,
Karimi, N.,
Nasr-Esfahani, E.,
Samavi, S.,
Soroushmehr, S.M.R.,
Ward, K.,
Najarian, K.,
Skin lesion segmentation in clinical images using deep learning,
ICPR16(337-342)
IEEE DOI
1705
Feature extraction, Image segmentation, Lesions, Lighting,
Machine learning, Malignant tumors, Skin, Melanoma,
convolutional neural network, deep learning,
medical image segmentation, skin, cancer
BibRef
Faraz, K.[Khuram],
Blondel, W.[Walter],
Amouroux, M.[Marine],
Daul, C.[Christian],
Towards skin image mosaicing,
IPTA16(1-6)
IEEE DOI
1703
Tele-dermatology.
feature extraction
BibRef
Majtner, T.,
Yildirim-Yayilgan, S.,
Hardeberg, J.Y.,
Combining deep learning and hand-crafted features for skin lesion
classification,
IPTA16(1-6)
IEEE DOI
1703
biomedical optical imaging
BibRef
Haji Rassouliha, A.,
Kmiecik, B.,
Taberner, A.J.,
Nash, M.P.,
Nielsen, P.M.F.,
A Low-cost, hand-held stereoscopic device for measuring dynamic
deformations of skin in vivo,
ICVNZ15(1-6)
IEEE DOI
1701
deformation
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Kawahara, J.[Jeremy],
Hamarneh, G.[Ghassan],
Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion
Trained Layers,
MLMI16(164-171).
Springer DOI
1611
BibRef
Bozorgtabar, B.[Behzad],
Abedini, M.[Mani],
Garnavi, R.[Rahil],
Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based
Refinement,
MLMI16(254-261).
Springer DOI
1611
BibRef
Kropidlowski, K.[Karol],
Kociolek, M.[Marcin],
Strzelecki, M.[Michal],
Czubinski, D.[Dariusz],
Blue Whitish Veil, Atypical Vascular Pattern and Regression Structures
Detection in Skin Lesions Images,
ICCVG16(418-428).
Springer DOI
1611
BibRef
Sun, X.X.[Xiao-Xiao],
Yang, J.F.[Ju-Feng],
Sun, M.[Ming],
Wang, K.[Kai],
A Benchmark for Automatic Visual Classification of Clinical Skin
Disease Images,
ECCV16(VI: 206-222).
Springer DOI
1611
BibRef
Bulan, O.,
Artan, Y.,
Wheal detection from skin prick test images using normalized-cuts and
region selection,
ICIP16(1250-1253)
IEEE DOI
1610
Calibration
BibRef
Schneider, D.,
Hecht, A.,
Photogrammetric 3d Acquisition And Analysis Of Medicamentous Induced
Pilomotor Reflex (goose Bumps),
ISPRS16(B5: 903-908).
DOI Link
1610
BibRef
Gonzalez-Castro, V.,
Debayle, J.,
Wazaefi, Y.,
Rahim, M.,
Gaudy, C.,
Grob, J.J.,
Fertil, B.,
Automatic classification of skin lesions using geometrical
measurements of adaptive neighborhoods and local binary patterns,
ICIP15(1722-1726)
IEEE DOI
1512
General adaptive neighborhoods
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Santos, A.[Anderson],
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Human Skin Segmentation Improved by Saliency Detection,
CAIP15(II:146-157).
Springer DOI
1511
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Rizzi, M.[Maria],
d'Aloia, M.[Matteo],
Cice, G.[Gianpaolo],
Computer Aided Evaluation (CAE) of Morphologic Changes in Pigmented
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ISCA15(250-257).
Springer DOI
1511
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Santos, A.[Anderson],
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Human Skin Segmentation Improved by Texture Energy Under Superpixels,
CIARP15(35-42).
Springer DOI
1511
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Kaur, P.[Parneet],
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Cula, G.O.[Gabriela Oana],
From photography to microbiology:
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BioImage15(1-10)
IEEE DOI
1510
Artificial neural networks
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Gupta, M.D.[Mithun Das],
Srinivasa, S.[Srinidhi],
Madhukara, J.,
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KL divergence based agglomerative clustering for automated Vitiligo
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CVPR15(2700-2709)
IEEE DOI
1510
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Koehoorn, J.[Joost],
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Diaconeasa, A.[Adriana],
Doshi, S.[Susan],
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Jalba, A.[Andrei],
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Automated Digital Hair Removal by Threshold Decomposition and
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ISMM15(15-26).
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1506
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IPTA12(207-211)
IEEE DOI
1503
cancer
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Toth, J.[Janos],
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ICIP14(3532-3536)
IEEE DOI
1502
Databases
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Lezoray, O.,
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ICIP14(897-901)
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1501
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Satat, G.,
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Skin perfusion photography,
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1411
biomedical optical imaging
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1410
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dots and globules, feature extraction, hair removal, melanoma;skin lesion
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1209
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Güçin, M.,
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Detection and Evaluation of Skin Disorders By One of Photogrammetric
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IEEE DOI
1207
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1205
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Pigmented skin lesion segmentation on macroscopic images,
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1201
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ICIP11(2801-2804).
IEEE DOI
1201
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1109
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IEEE DOI
1010
Orientation Sensitive Fuzzy C-means algorithm (OS-FCM)
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Placidi, G.[Giuseppe],
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Cinque, B.[Benedetta],
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La Torre, C.[Cristina],
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Endoscopy .