Chaudhuri, B.B.,
Rodenacker, K.,
Burger, G.,
Characterization and Featuring of Histological Section Images,
PRL(7), 1988, pp. 245-252.
BibRef
8800
Bartels, P.H.,
Gahm, T.,
Thompson, D.,
Automated Microscopy in Diagnostic Histopathology:
From Image-Processing to Automated Reasoning,
IJIST(8), No. 2, 1997, pp. 214-223.
9704
BibRef
Adiga, P.S.U.[P.S. Umesh],
Chaudhuri, B.B.,
An efficient method based on watershed and rule-based merging for
segmentation of 3-D histo-pathological images,
PR(34), No. 7, July 2001, pp. 1449-1458.
Elsevier DOI
0105
BibRef
Gurcan, M.N.[Metin N.],
Boucheron, L.[Laura],
Can, A.[Ali],
Madabhushi, A.[Anant],
Rajpoot, N.[Nasir],
Yener, B.[Bulent],
Histopathological Image Analysis: A Review,
RevBiomedEng(2), 2009, pp. 147-171.
IEEE DOI
WWW Link.
Survey, Histopathology.
BibRef
0900
Brenner, J.F.[John F.],
Lester, J.M.[James M.],
Selles, W.D.[William D.],
Scene Segmentation in Automated Histopathology:
Techniques Evolved from Cytology Automation,
PR(13), No. 1, 1981, pp. 65-77.
Elsevier DOI
0309
BibRef
Sertel, O.,
Kong, J.,
Shimada, H.,
Catalyurek, U.V.,
Saltz, J.H.,
Gurcan, M.N.,
Computer-aided prognosis of neuroblastoma on whole-slide images:
Classification of stromal development,
PR(42), No. 6, June 2009, pp. 1093-1103.
Elsevier DOI
0902
Whole-slide histopathological image analysis; Texture analysis; Neuroblastoma
BibRef
Kong, J.[Jun],
Sertel, O.[Olcay],
Shimada, H.[Hiroyuki],
Boyer, K.L.[Kim L.],
Saltz, J.[Joel],
Gurcan, M.N.[Metin N.],
Computer-aided evaluation of neuroblastoma on whole-slide histology
images: Classifying grade of neuroblastic differentiation,
PR(42), No. 6, June 2009, pp. 1080-1092.
Elsevier DOI
0902
BibRef
Earlier:
Computer-Aided Grading of Neuroblastic Differentiation:
Multi-Resolution and Multi-Classifier Approach,
ICIP07(V: 525-528).
IEEE DOI
0709
Quantitative image analysis; Microscopy images; Neuroblastoma
prognosis; Grade of differentiation; Multi-resolution pathological
image analysis; Machine learning
BibRef
Dundar, M.M.[M. Murat],
Badve, S.I.[Sun-Il],
Raykar, V.C.[Vikas C.],
Jain, R.K.[Rohit K.],
Sertel, O.[Olcay],
Gurcan, M.N.[Metin N.],
A Multiple Instance Learning Approach toward Optimal Classification of
Pathology Slides,
ICPR10(2732-2735).
IEEE DOI
1008
BibRef
Kong, H.,
Gurcan, M.,
Belkacem-Boussaid, K.,
Partitioning Histopathological Images: An Integrated Framework for
Supervised Color-Texture Segmentation and Cell Splitting,
MedImg(30), No. 9, September 2011, pp. 1661-1677.
IEEE DOI
1109
BibRef
Ali, S.,
Madabhushi, A.,
An Integrated Region-, Boundary-, Shape-Based Active Contour for
Multiple Object Overlap Resolution in Histological Imagery,
MedImg(31), No. 7, July 2012, pp. 1448-1460.
IEEE DOI
1208
BibRef
Loménie, N.[Nicolas],
Racoceanu, D.[Daniel],
Point set morphological filtering and semantic spatial configuration
modeling: Application to microscopic image and bio-structure analysis,
PR(45), No. 8, August 2012, pp. 2894-2911.
Elsevier DOI
1204
Shape analysis; Mesh analysis; Unorganized point set; Spatial relation
modeling; Mathematical morphological operator; Image exploration; Graph
representation; Semantic query; Visual reasoning; Digital
histopathology
BibRef
Srinivas, U.,
Mousavi, H.S.,
Monga, V.,
Hattel, A.,
Jayarao, B.,
Simultaneous Sparsity Model for Histopathological Image
Representation and Classification,
MedImg(33), No. 5, May 2014, pp. 1163-1179.
IEEE DOI
1405
Biomedical image processing
BibRef
Gultekin, T.,
Koyuncu, C.F.,
Sokmensuer, C.,
Gunduz-Demir, C.,
Two-Tier Tissue Decomposition for Histopathological Image
Representation and Classification,
MedImg(34), No. 1, January 2015, pp. 275-283.
IEEE DOI
1502
biological organs
BibRef
Vu, T.H.,
Mousavi, H.S.,
Monga, V.,
Rao, G.,
Rao, U.K.A.,
Histopathological Image Classification Using Discriminative
Feature-Oriented Dictionary Learning,
MedImg(35), No. 3, March 2016, pp. 738-751.
IEEE DOI
1603
Biomedical imaging
BibRef
Su, H.,
Xing, F.,
Yang, L.,
Robust Cell Detection of Histopathological Brain Tumor Images Using
Sparse Reconstruction and Adaptive Dictionary Selection,
MedImg(35), No. 6, June 2016, pp. 1575-1586.
IEEE DOI
1606
Dictionaries
BibRef
Shi, X.S.[Xiao-Shuang],
Sapkota, M.[Manish],
Xing, F.Y.[Fu-Yong],
Liu, F.J.[Fu-Jun],
Cui, L.[Lei],
Yang, L.[Lin],
Pairwise based deep ranking hashing for histopathology image
classification and retrieval,
PR(81), 2018, pp. 14-22.
Elsevier DOI
1806
Histopathology images, Classification, Retrieval,
Ranking hashing, Deep learning
BibRef
Zhu, S.J.[Shu-Jin],
Li, Y.H.[Yue-Hua],
Kalra, S.[Shivam],
Tizhoosh, H.R.,
Multiple disjoint dictionaries for representation of histopathology
images,
JVCIR(55), 2018, pp. 243-252.
Elsevier DOI
1809
Image retrieval, Image representation, Histopathology,
Wholeslide imaging, Bag-of-words, Dictionary learning, LBP, SVM, Deep learning
BibRef
Kumar, N.[Neeraj],
Uppala, P.[Phanikrishna],
Duddu, K.[Karthik],
Sreedhar, H.[Hari],
Varma, V.[Vishal],
Guzman, G.[Grace],
Walsh, M.[Michael],
Sethi, A.[Amit],
Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and
Hierarchical Clustering,
MedImg(38), No. 5, May 2019, pp. 1304-1313.
IEEE DOI
1905
Image segmentation, Imaging, Diseases, Spatial resolution, Chemicals,
Biological tissues, Quantum cascade lasers,
hierarchical clustering
BibRef
Lahiani, A.[Amal],
Gildenblat, J.[Jacob],
Klaman, I.[Irina],
Navab, N.[Nassir],
Klaiman, E.[Eldad],
Generalising multistain immunohistochemistry tissue segmentation using
end-to-end colour deconvolution deep neural networks,
IET-IPR(13), No. 7, 30 May 2019, pp. 1066-1073.
DOI Link
1906
BibRef
Katouzian, A.[Amin],
Karamalis, A.[Athanasios],
Lisauskas, J.[Jennifer],
Eslami, A.[Abouzar],
Navab, N.[Nassir],
IVUS-Histology Image Registration,
WBIR12(141-149).
Springer DOI
1208
BibRef
Maji, P.,
Mahapatra, S.,
Circular Clustering in Fuzzy Approximation Spaces for Color
Normalization of Histological Images,
MedImg(39), No. 5, May 2020, pp. 1735-1745.
IEEE DOI
2005
Image color analysis, Histograms, Image analysis,
Clustering algorithms, Rough sets, Uncertainty, Fuzzy sets,
rough sets
BibRef
Li, X.[Xiao],
Tang, H.Z.[Hong-Zhong],
Zhang, D.B.[Dong-Bo],
Liu, T.[Ting],
Mao, L.Z.[Li-Zhen],
Chen, T.Y.[Tian-Yu],
Histopathological Image Classification Through Discriminative Feature
Learning and Mutual Information-Based Multi-Channel Joint Sparse
Representation,
JVCIR(70), 2020, pp. 102799.
Elsevier DOI
2007
Discriminative feature learning,
Stack-based discriminative prediction sparse decomposition (SDPSD),
Histopathological image classification
BibRef
Vu, T.,
Lai, P.,
Raich, R.,
Pham, A.,
Fern, X.Z.,
Rao, U.A.,
A Novel Attribute-Based Symmetric Multiple Instance Learning for
Histopathological Image Analysis,
MedImg(39), No. 10, October 2020, pp. 3125-3136.
IEEE DOI
2010
Cancer, Image analysis, Training, Task analysis,
Support vector machines, Image segmentation,
dynamic programming
BibRef
Mahmood, F.,
Borders, D.,
Chen, R.J.,
Mckay, G.N.,
Salimian, K.J.,
Baras, A.,
Durr, N.J.,
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in
Histopathology Images,
MedImg(39), No. 11, November 2020, pp. 3257-3267.
IEEE DOI
2011
Image segmentation, Pathology, Training, Diseases, Task analysis,
Generative adversarial networks, Morphology, Nuclei segmentation,
synthetic pathology data
BibRef
Shafiei, S.,
Safarpoor, A.,
Jamalizadeh, A.,
Tizhoosh, H.R.,
Class-Agnostic Weighted Normalization of Staining in Histopathology
Images Using a Spatially Constrained Mixture Model,
MedImg(39), No. 11, November 2020, pp. 3355-3366.
IEEE DOI
2011
Image color analysis, Parameter estimation, Pathology,
Gaussian mixture model, Probability density function,
spatial information
BibRef
Qu, H.,
Wu, P.,
Huang, Q.,
Yi, J.,
Yan, Z.,
Li, K.,
Riedlinger, G.M.,
De, S.,
Zhang, S.,
Metaxas, D.N.,
Weakly Supervised Deep Nuclei Segmentation Using Partial Points
Annotation in Histopathology Images,
MedImg(39), No. 11, November 2020, pp. 3655-3666.
IEEE DOI
2011
Image segmentation, Annotations, Training, Task analysis, Cancer,
Biomedical imaging, Deep learning, Nuclei detection,
conditional random field
BibRef
Graham, S.,
Epstein, D.,
Rajpoot, N.,
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in
Histology Images,
MedImg(39), No. 12, December 2020, pp. 4124-4136.
IEEE DOI
2012
Image segmentation, Standards, Task analysis, Pathology,
Harmonic analysis, Computer architecture, Machine learning,
computational pathology
BibRef
Gunesli, G.N.,
Sokmensuer, C.,
Gunduz-Demir, C.,
AttentionBoost: Learning What to Attend for Gland Segmentation in
Histopathological Images by Boosting Fully Convolutional Networks,
MedImg(39), No. 12, December 2020, pp. 4262-4273.
IEEE DOI
2012
Glands, Task analysis, Image segmentation, Adaptation models,
Training, Boosting, Deep learning,
instance segmentation
BibRef
Zheng, Y.,
Jiang, Z.,
Xie, F.,
Shi, J.,
Zhang, H.,
Huai, J.,
Cao, M.,
Yang, X.,
Diagnostic Regions Attention Network (DRA-Net) for Histopathology WSI
Recommendation and Retrieval,
MedImg(40), No. 3, March 2021, pp. 1090-1103.
IEEE DOI
2103
Histopathology, Cancer, Feature extraction, Databases,
Solid modeling, Image analysis, Annotations, Digital pathology, RNN
BibRef
Koohbanani, N.A.[Navid Alemi],
Unnikrishnan, B.[Balagopal],
Khurram, S.A.[Syed Ali],
Krishnaswamy, P.[Pavitra],
Rajpoot, N.[Nasir],
Self-Path: Self-Supervision for Classification of Pathology Images
With Limited Annotations,
MedImg(40), No. 10, October 2021, pp. 2845-2856.
IEEE DOI
2110
Task analysis, Annotations, Histopathology,
Semisupervised learning, Training, Tumors, Labeling, domain adaptation
BibRef
Mahapatra, D.[Dwarikanath],
Poellinger, A.[Alexander],
Shao, L.[Ling],
Reyes, M.[Mauricio],
Interpretability-Driven Sample Selection Using Self Supervised
Learning for Disease Classification and Segmentation,
MedImg(40), No. 10, October 2021, pp. 2548-2562.
IEEE DOI
2110
Uncertainty, Feature extraction, Histograms, Training,
Image segmentation, Histopathology, Entropy, Interpretability,
Histopathology segmentation
BibRef
Song, J.[Jie],
Xiao, L.[Liang],
Molaei, M.[Mohsen],
Lian, Z.C.[Zhi-Chao],
Sparse Coding Driven Deep Decision Tree Ensembles for Nucleus
Segmentation in Digital Pathology Images,
IP(30), 2021, pp. 8088-8101.
IEEE DOI
2110
Pathology, Image segmentation, Decoding, Decision trees,
Convolutional codes, Feature extraction, Computer architecture,
feature reuse
BibRef
Adu, K.[Kwabena],
Yu, Y.B.[Yong-Bin],
Cai, J.Y.[Jing-Ye],
Owusu-Agyemang, K.[Kwabena],
Twumasi, B.A.[Baidenger Agyekum],
Wang, X.X.[Xiang-Xiang],
DHS-CapsNet: Dual horizontal squash capsule networks for lung and
colon cancer classification from whole slide histopathological images,
IJIST(31), No. 4, 2021, pp. 2075-2092.
DOI Link
2112
artificial intelligence, capsule network, colon cancer,
convolutional neural network, histopathological images, lung cancer
BibRef
Chen, Z.N.[Zhi-Neng],
Zhao, S.[Shuai],
Hu, K.[Kai],
Han, J.[Jing],
Ji, Y.[Yuan],
Ling, S.P.[Shao-Ping],
Gao, X.[Xieping],
A hierarchical and multi-view registration of serial
histopathological images,
PRL(152), 2021, pp. 210-217.
Elsevier DOI
2112
Image registration, Histopathological image, Multi-view,
Elastic registration, Biomarker colocalization
BibRef
Xie, Y.T.[Yu-Tong],
Zhang, J.P.[Jian-Peng],
Liao, Z.B.[Zhi-Bin],
Verjans, J.[Johan],
Shen, C.H.[Chun-Hua],
Xia, Y.[Yong],
Intra- and Inter-Pair Consistency for Semi-Supervised Gland
Segmentation,
IP(31), 2022, pp. 894-905.
IEEE DOI
2201
Glands, Image segmentation, Semantics, Feature extraction,
Histopathology, Training, Data models, Gland segmentation,
deep convolutional neural network
BibRef
Belharbi, S.[Soufiane],
Rony, J.[Jérôme],
Dolz, J.[Jose],
Ben Ayed, I.[Ismail],
Mccaffrey, L.[Luke],
Granger, E.[Eric],
Deep Interpretable Classification and Weakly-Supervised Segmentation
of Histology Images via Max-Min Uncertainty,
MedImg(41), No. 3, March 2022, pp. 702-714.
IEEE DOI
2203
Image segmentation, Uncertainty, Histopathology, Predictive models,
Standards, Training, Solid modeling, interpretability
BibRef
Zhu, C.[Chuang],
Chen, W.K.[Wen-Kai],
Peng, T.[Ting],
Wang, Y.[Ying],
Jin, M.[Mulan],
Hard Sample Aware Noise Robust Learning for Histopathology Image
Classification,
MedImg(41), No. 4, April 2022, pp. 881-894.
IEEE DOI
2204
Noise measurement, Training, Histopathology, Noise robustness,
Image classification, Data models, Predictive models,
label correction
BibRef
Li, W.Y.[Wen-Yuan],
Li, J.[Jiayun],
Wang, Z.C.[Zi-Chen],
Polson, J.[Jennifer],
Sisk, A.E.[Anthony E.],
Sajed, D.P.[Dipti P.],
Speier, W.[William],
Arnold, C.W.[Corey W.],
PathAL: An Active Learning Framework for Histopathology Image
Analysis,
MedImg(41), No. 5, May 2022, pp. 1176-1187.
IEEE DOI
2205
Noise measurement, Annotations, Training, Biomedical imaging,
Uncertainty, Image segmentation, Task analysis,
curriculum learning
BibRef
Chattopadhyay, A.[Aratrik],
Paul, A.[Angshuman],
Mukherjee, D.P.[Dipti Prasad],
Detail preserving conditional random field as 2-D RNN for gland
segmentation in histology images,
PRL(159), 2022, pp. 38-45.
Elsevier DOI
2206
2-D RNN, Conditional random field, Detail preservation,
Gland segmentation, Histology
BibRef
Xiang, T.[Tiange],
Song, Y.[Yang],
Zhang, C.Y.[Chao-Yi],
Liu, D.[Dongnan],
Chen, M.[Mei],
Zhang, F.[Fan],
Huang, H.[Heng],
O'Donnell, L.[Lauren],
Cai, W.D.[Wei-Dong],
DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel
Pathology Image Analysis,
MedImg(41), No. 8, August 2022, pp. 2180-2190.
IEEE DOI
2208
Visualization, Encoding, Annotations, Redundancy, Pathology,
Metastasis, Image analysis, Weakly-supervised training,
whole slide images
BibRef
Xu, Y.Z.[Yong-Zhao],
dos Santos, M.A.[Matheus A.],
Souza, L.F.F.[Luís Fabrício F.],
Marques, A.G.[Adriell G.],
Zhang, L.J.[Li-Juan],
da Costa Nascimento, J.J.[José Jerovane],
de Albuquerque, V.H.C.[Victor Hugo C.],
Filho, P.P.R.[Pedro P. Rebouças],
New fully automatic approach for tissue identification in
histopathological examinations using transfer learning,
IET-IPR(16), No. 11, 2022, pp. 2875-2889.
DOI Link
2208
BibRef
Lin, J.[Jiatai],
Han, G.Q.[Guo-Qiang],
Pan, X.P.[Xi-Peng],
Liu, Z.[Zaiyi],
Chen, H.[Hao],
Li, D.[Danyi],
Jia, X.P.[Xi-Ping],
Shi, Z.W.[Zhen-Wei],
Wang, Z.Z.[Zhi-Zhen],
Cui, Y.F.[Yan-Fen],
Li, H.M.[Hai-Ming],
Liang, C.H.[Chang-Hong],
Liang, L.[Li],
Wang, Y.[Ying],
Han, C.[Chu],
PDBL: Improving Histopathological Tissue Classification With
Plug-and-Play Pyramidal Deep-Broad Learning,
MedImg(41), No. 9, September 2022, pp. 2252-2262.
IEEE DOI
2209
Feature extraction, Computational modeling, Training,
Adaptation models, Biomedical imaging, Annotations, Deep learning,
broad learning system
BibRef
Ge, L.[Lin],
Wei, X.Y.[Xing-Yue],
Hao, Y.[Yayu],
Luo, J.W.[Jian-Wen],
Xu, Y.[Yan],
Unsupervised Histological Image Registration Using Structural Feature
Guided Convolutional Neural Network,
MedImg(41), No. 9, September 2022, pp. 2414-2431.
IEEE DOI
2209
Image registration, Strain, Convolutional neural networks,
Task analysis, Image resolution, Feature extraction,
unsupervised learning
BibRef
Zhang, Y.L.[Yun-Long],
Lin, X.[Xin],
Zhuang, Y.H.[Yi-Hong],
Sun, L.Y.[Li-Yan],
Huang, Y.[Yue],
Ding, X.[Xinghao],
Wang, G.S.[Gui-Sheng],
Yang, L.[Lin],
Yu, Y.Z.[Yi-Zhou],
Harmonizing Pathological and Normal Pixels for Pseudo-Healthy
Synthesis,
MedImg(41), No. 9, September 2022, pp. 2457-2468.
IEEE DOI
2209
Pathology, Image segmentation, Training, Lesions, Biomedical imaging,
Generators, Measurement, Medical image synthesis,
label noise
BibRef
Zheng, Y.[Yi],
Gindra, R.H.[Rushin H.],
Green, E.J.[Emily J.],
Burks, E.J.[Eric J.],
Betke, M.[Margrit],
Beane, J.E.[Jennifer E.],
Kolachalama, V.B.[Vijaya B.],
A Graph-Transformer for Whole Slide Image Classification,
MedImg(41), No. 11, November 2022, pp. 3003-3015.
IEEE DOI
2211
Pathology, Feature extraction, Transformers, Tumors, Deep learning,
Training, Lung, Digital pathology, graph convolutional network,
lung cancer
BibRef
Shen, Y.Q.[Yi-Qing],
Shen, D.G.[Ding-Gang],
Ke, J.[Jing],
Identify Representative Samples by Conditional Random Field of Cancer
Histology Images,
MedImg(41), No. 12, December 2022, pp. 3835-3848.
IEEE DOI
2212
Histopathology, Training, Task analysis,
Convolutional neural networks, Deep learning, Correlation, active learning
BibRef
Zhang, W.H.[Wen-Hua],
Zhang, J.[Jun],
Yang, S.[Sen],
Wang, X.[Xiyue],
Yang, W.[Wei],
Huang, J.Z.[Jun-Zhou],
Wang, W.P.[Wen-Ping],
Han, X.[Xiao],
Knowledge-Based Representation Learning for Nucleus Instance
Classification From Histopathological Images,
MedImg(41), No. 12, December 2022, pp. 3939-3951.
IEEE DOI
2212
Pathology, Task analysis, Feature extraction, Data models,
Representation learning, Labeling, Annotations, Triplet learning,
digital pathology
BibRef
Gao, Z.[Zeyu],
Jia, C.[Chang],
Li, Y.[Yang],
Zhang, X.L.[Xian-Li],
Hong, B.Y.[Bang-Yang],
Wu, J.[Jialun],
Gong, T.L.[Tie-Liang],
Wang, C.B.[Chun-Bao],
Meng, D.Y.[De-Yu],
Zheng, Y.F.[Ye-Feng],
Li, C.[Chen],
Unsupervised Representation Learning for Tissue Segmentation in
Histopathological Images: From Global to Local Contrast,
MedImg(41), No. 12, December 2022, pp. 3611-3623.
IEEE DOI
2212
Task analysis, Image segmentation, Annotations, Decoding, Tumors,
Representation learning, Cancer, Contrastive learning, superpixel
BibRef
Yang, M.[Mei],
Xie, Z.[Zhiying],
Wang, Z.X.[Zhao-Xia],
Yuan, Y.[Yun],
Zhang, J.[Jue],
Su-MICL: Severity-Guided Multiple Instance Curriculum Learning for
Histopathology Image Interpretable Classification,
MedImg(41), No. 12, December 2022, pp. 3533-3543.
IEEE DOI
2212
Histopathology, Lesions, Training, Diseases, Annotations,
Task analysis, Supervised learning, Multiple instance learning,
interpretability
BibRef
Wang, Z.Z.[Zhen-Zhen],
Saoud, C.[Carla],
Wangsiricharoen, S.[Sintawat],
James, A.W.[Aaron W.],
Popel, A.S.[Aleksander S.],
Sulam, J.[Jeremias],
Label Cleaning Multiple Instance Learning: Refining Coarse
Annotations on Single Whole-Slide Images,
MedImg(41), No. 12, December 2022, pp. 3952-3968.
IEEE DOI
2212
Annotations, Cancer, Pathology, Training, Tumors, Refining,
Predictive models, Whole-slide image segmentation, label cleaning
BibRef
Chen, Y.[Yi],
Dong, Y.[Yang],
Si, L.[Lu],
Yang, W.M.[Wen-Ming],
Du, S.[Shan],
Tian, X.[Xuewu],
Li, C.[Chao],
Liao, Q.M.[Qing-Min],
Ma, H.[Hui],
Dual Polarization Modality Fusion Network for Assisting Pathological
Diagnosis,
MedImg(42), No. 1, January 2023, pp. 304-316.
IEEE DOI
2301
Cancer, Pathology, Imaging, Feature extraction, Microstructure,
Optical switches, Image classification, switched attention
BibRef
Lou, W.[Wei],
Li, H.F.[Hao-Feng],
Li, G.B.[Guan-Bin],
Han, X.G.[Xiao-Guang],
Wan, X.[Xiang],
Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation
Framework,
MedImg(42), No. 4, April 2023, pp. 947-958.
IEEE DOI
2304
Image segmentation, Training, Labeling, Annotations, Histopathology,
Generative adversarial networks, Big Data, Nuclei segmentation,
generative adversarial networks
BibRef
Sabban, D.[David],
Shimshoni, I.[Ilan],
Segmenting Glandular Biopsy Images Using the Separate Merged Objects
Algorithm,
MCV22(466-481).
Springer DOI
2304
BibRef
Ding, M.[Meidan],
Qu, A.[Aiping],
Zhong, H.Q.[Hai-Qin],
Lai, Z.H.[Zhi-Hui],
Xiao, S.[Shuomin],
He, P.[Penghui],
An enhanced vision transformer with wavelet position embedding for
histopathological image classification,
PR(140), 2023, pp. 109532.
Elsevier DOI
2305
Histopathological image classification, Vision transformer,
Convolutional neural network, Wavelet position embedding,
External multi-head attention
BibRef
Kadirappa, R.[Ravindranath],
Subbian, D.[Deivalakshmi],
Ramasamy, P.[Pandeeswari],
Ko, S.B.[Seok-Bum],
Histopathological carcinoma classification using parallel,
cross-concatenated and grouped convolutions deep neural network,
IJIST(33), No. 3, 2023, pp. 1048-1061.
DOI Link
2305
colon adenocarcinoma, deep learning, hepatocellular carcinoma,
lung adenocarcinoma, lung squamous carcinoma
BibRef
Jiang, Y.H.[Ying-Hai],
Cui, R.[Rongsheng],
Liu, F.[Feng],
Multi-resolutional human visual perception optimized pathology image
progressive coding based on JPEG2000,
SP:IC(115), 2023, pp. 116960.
Elsevier DOI
2306
Image coding, Multi-resolution, JPEG2000, Perception-based image quality,
Visibility threshold, Whole-slide pathology image
BibRef
Yu, J.G.[Jin-Gang],
Wu, Z.[Zihao],
Ming, Y.[Yu],
Deng, S.[Shule],
Wu, Q.H.[Qi-Hang],
Xiong, Z.T.[Zhong-Tang],
Yu, T.Y.[Tian-You],
Xia, G.S.[Gui-Song],
Jiang, Q.P.[Qing-Ping],
Li, Y.Q.[Yuan-Qing],
Bayesian Collaborative Learning for Whole-Slide Image Classification,
MedImg(42), No. 6, June 2023, pp. 1809-1821.
IEEE DOI
2306
Federated learning, Bayes methods, Pathology, Training,
Probabilistic logic, Task analysis, Graphics processing units,
multiple instance learning (MIL)
BibRef
Mahapatra, S.[Suman],
Maji, P.[Pradipta],
Truncated Normal Mixture Prior Based Deep Latent Model for Color
Normalization of Histology Images,
MedImg(42), No. 6, June 2023, pp. 1746-1757.
IEEE DOI
2306
Image color analysis, Histopathology, Data mining,
Biological system modeling, Image coding, Image analysis,
truncated normal mixture model
BibRef
Hosseini, S.M.[S. Maryam],
Sikaroudi, M.[Milad],
Babaie, M.[Morteza],
Tizhoosh, H.R.,
Proportionally Fair Hospital Collaborations in Federated Learning of
Histopathology Images,
MedImg(42), No. 7, July 2023, pp. 1982-1995.
IEEE DOI
2307
Federated learning, Hospitals, Training, Data models, Servers,
Histopathology, Optimization
BibRef
Shen, Y.Q.[Yi-Qing],
Sowmya, A.[Arcot],
Luo, Y.L.[Yu-Lin],
Liang, X.Y.[Xiao-Yao],
Shen, D.G.[Ding-Gang],
Ke, J.[Jing],
A Federated Learning System for Histopathology Image Analysis With an
Orchestral Stain-Normalization GAN,
MedImg(42), No. 7, July 2023, pp. 1969-1981.
IEEE DOI
2307
Histopathology, Training, Generators, Federated learning, Servers,
Generative adversarial networks, Cancer, Federated learning,
stain normalization
BibRef
Li, S.R.[Sheng-Rui],
Zhao, Y.N.[Yi-Ning],
Zhang, J.[Jun],
Yu, T.[Ting],
Zhang, J.[Ji],
Gao, Y.[Yue],
High-Order Correlation-Guided Slide-Level Histology Retrieval With
Self-Supervised Hashing,
PAMI(45), No. 9, September 2023, pp. 11008-11023.
IEEE DOI
2309
BibRef
Zheng, Y.S.[Yu-Shan],
Li, J.[Jun],
Shi, J.[Jun],
Xie, F.[Fengying],
Huai, J.G.[Jian-Guo],
Cao, M.[Ming],
Jiang, Z.G.[Zhi-Guo],
Kernel Attention Transformer for Histopathology Whole Slide Image
Analysis and Assistant Cancer Diagnosis,
MedImg(42), No. 9, September 2023, pp. 2726-2739.
IEEE DOI
2310
BibRef
Li, Z.Y.[Zhong-Yu],
Li, C.Q.[Chao-Qun],
Luo, X.D.[Xiang-De],
Zhou, Y.T.[Yi-Tian],
Zhu, J.[Jihua],
Xu, C.[Cunbao],
Yang, M.[Meng],
Wu, Y.[Yenan],
Chen, Y.F.[Yi-Feng],
Toward Source-Free Cross Tissues Histopathological Cell Segmentation
via Target-Specific Finetuning,
MedImg(42), No. 9, September 2023, pp. 2666-2677.
IEEE DOI
2310
BibRef
Wang, Z.[Zhao],
Feng, Q.Y.[Qian-Yu],
Corredor, G.[Germán],
Koyuncu, C.[Can],
Lu, C.[Cheng],
Measuring dense false positive regions from segmentation result for
whole slide tissue histology image,
JVCIR(96), 2023, pp. 103929.
Elsevier DOI
2310
Image segmentation, Evaluation metric, Histology image
BibRef
Sayaheen, Y.O.[Yasmeen O.],
Texture-based approach to classification meningioma using pathology
images,
IJCVR(13), No. 6, 2023, pp. 677-692.
DOI Link
2310
BibRef
Yu, J.H.[Jia-Hui],
Ma, T.Y.[Tian-Yu],
Chen, H.[Hang],
Lai, M.[Maode],
Ju, Z.J.[Zhao-Jie],
Xu, Y.K.[Ying-Ke],
Marrying Global-Local Spatial Context for Image Patches in
Computer-Aided Assessment,
SMCS(53), No. 11, November 2023, pp. 7099-7111.
IEEE DOI
2310
BibRef
Chan, T.H.[Tsai Hor],
Cendra, F.J.[Fernando Julio],
Ma, L.[Lan],
Yin, G.S.[Guo-Sheng],
Yu, L.[Lequan],
Histopathology Whole Slide Image Analysis with Heterogeneous Graph
Representation Learning,
CVPR23(15661-15670)
IEEE DOI
2309
BibRef
Lin, T.[Tiancheng],
Yu, Z.[Zhimiao],
Hu, H.Y.[Hong-Yu],
Xu, Y.[Yi],
Chen, C.W.[Chang Wen],
Interventional Bag Multi-Instance Learning On Whole-Slide
Pathological Images,
CVPR23(19830-19839)
IEEE DOI
2309
BibRef
Chen, Y.C.[Yuan-Chih],
Lu, C.S.[Chun-Shien],
RankMix: Data Augmentation for Weakly Supervised Learning of
Classifying Whole Slide Images with Diverse Sizes and Imbalanced
Categories,
CVPR23(23936-23945)
IEEE DOI
2309
BibRef
Li, H.[Honglin],
Zhu, C.[Chenglu],
Zhang, Y.L.[Yun-Long],
Sun, Y.X.[Yu-Xuan],
Shui, Z.Y.[Zhong-Yi],
Kuang, W.W.[Wen-Wei],
Zheng, S.[Sunyi],
Yang, L.[Lin],
Task-Specific Fine-Tuning via Variational Information Bottleneck for
Weakly-Supervised Pathology Whole Slide Image Classification,
CVPR23(7454-7463)
IEEE DOI
2309
BibRef
Kang, M.[Mingu],
Song, H.[Heon],
Park, S.[Seonwook],
Yoo, D.G.[Dong-Geun],
Pereira, S.[Sérgio],
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets,
CVPR23(3344-3354)
IEEE DOI
2309
BibRef
Lu, M.Y.[Ming Y.],
Chen, B.[Bowen],
Zhang, A.[Andrew],
Williamson, D.F.K.[Drew F.K.],
Chen, R.J.[Richard J.],
Ding, T.[Tong],
Le, L.P.[Long Phi],
Chuang, Y.S.[Yung-Sung],
Mahmood, F.[Faisal],
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for
Histopathology Images,
CVPR23(19764-19775)
IEEE DOI
2309
BibRef
Qin, W.K.[Wen-Kang],
Xu, R.[Rui],
Jiang, S.[Shan],
Jiang, T.T.[Ting-Ting],
Luo, L.[Lin],
Pathtr: Context-aware Memory Transformer for Tumor Localization in
Gigapixel Pathology Images,
ACCV22(VI:115-131).
Springer DOI
2307
BibRef
Wang, Q.[Qian],
Chen, Z.[Zhao],
A Deep Wavelet Network for High-resolution Microscopy Hyperspectral
Image Reconstruction,
MIA-COVID19D22(648-662).
Springer DOI
2304
BibRef
Singh, P.[Pranav],
Cirrone, J.[Jacopo],
A Data-efficient Deep Learning Framework for Segmentation and
Classification of Histopathology Images,
MCV22(385-405).
Springer DOI
2304
BibRef
Wibawa, M.S.[Made Satria],
Lo, K.W.[Kwok-Wai],
Young, L.S.[Lawrence S.],
Rajpoot, N.[Nasir],
Multi-scale Attention-based Multiple Instance Learning for
Classification of Multi-gigapixel Histology Images,
MIA-COVID19D22(635-647).
Springer DOI
2304
BibRef
Mormont, R.[Romain],
Testouri, M.[Mehdi],
Marée, R.[Raphaël],
Geurts, P.[Pierre],
Relieving Pixel-wise Labeling Effort for Pathology Image Segmentation
with Self-training,
MIA-COVID19D22(577-592).
Springer DOI
2304
BibRef
Kang, C.M.[Chol-Min],
Lee, C.G.[Chung-Gi],
Song, H.[Heon],
Ma, M.[Minuk],
Pereira, S.[Sérgio],
Variability Matters: Evaluating Inter-rater Variability in
Histopathology for Robust Cell Detection,
MIA-COVID19D22(552-565).
Springer DOI
2304
BibRef
Wölflein, G.[Georg],
Um, I.H.[In Hwa],
Harrison, D.J.[David J.],
Arandjelovic, O.[Ognjen],
HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial
Networks,
WACV23(4986-4996)
IEEE DOI
2302
Measurement, Generative adversarial networks, Task analysis,
Signal to noise ratio, Cancer.
BibRef
Stegmüller, T.[Thomas],
Bozorgtabar, B.[Behzad],
Spahr, A.[Antoine],
Thiran, J.P.[Jean-Philippe],
ScoreNet: Learning Non-Uniform Attention and Augmentation for
Transformer-Based Histopathological Image Classification,
WACV23(6159-6168)
IEEE DOI
2302
Costs, Histopathology, Semantics, Transformers, Throughput,
Applications: Biomedical/healthcare/medicine
BibRef
Liu, K.[Kechun],
Li, B.[Beibin],
Wu, W.J.[Wen-Jun],
May, C.[Caitlin],
Chang, O.[Oliver],
Knezevich, S.[Stevan],
Reisch, L.[Lisa],
Elmore, J.[Joann],
Shapiro, L.[Linda],
VSGD-Net: Virtual Staining Guided Melanocyte Detection on
Histopathological Images,
WACV23(1918-1927)
IEEE DOI
2302
Visualization, Pathology, Image synthesis,
Biological system modeling, Source coding, Biopsy, Melanoma,
visual reasoning
BibRef
Moghadam, P.A.[Puria Azadi],
van Dalen, S.[Sanne],
Martin, K.C.[Karina C.],
Lennerz, J.[Jochen],
Yip, S.[Stephen],
Farahani, H.[Hossein],
Bashashati, A.[Ali],
A Morphology Focused Diffusion Probabilistic Model for Synthesis of
Histopathology Images,
WACV23(1999-2008)
IEEE DOI
2302
Visualization, Histopathology, Image color analysis,
Computational modeling, Microscopy, Morphology, Brain modeling,
Applications: Biomedical/healthcare/medicine
BibRef
Guan, R.W.[Run-Wei],
Fei, Y.H.[Yan-Hua],
Zhu, X.H.[Xiao-Hui],
Yao, S.L.[Shan-Liang],
Yue, Y.[Yong],
Ma, J.M.[Jie-Ming],
CPNet: A Hybrid Neural Network for Identification of Carcinoma
Pathological Slices,
ICIVC22(599-604)
IEEE DOI
2301
Training, Deep learning, Pathology, Costs, Codes, Computational modeling,
Transfer learning, intelligent medicine, CNN-ViT hybrid NN
BibRef
Teh, E.W.[Eu Wern],
Taylor, G.W.[Graham W.],
Understanding the impact of image and input resolution on deep
digital pathology patch classifiers,
CRV22(159-166)
IEEE DOI
2301
Pathology, Image resolution, Correlation, Annotations, Data models, Tuning,
Robots, Digital Pathology, Patch Classification, Annotation-efficient Learning
BibRef
Li, M.[Meng],
Li, C.Y.[Chao-Yi],
Hobson, P.[Peter],
Jennings, T.[Tony],
Lovell, B.C.[Brian C.],
MedViTGAN: End-to-End Conditional GAN for Histopathology Image
Augmentation with Vision Transformers,
ICPR22(4406-4413)
IEEE DOI
2212
Training, Adaptation models, Histopathology, Image synthesis,
Semantic segmentation, Computer architecture, Transformers, Vision transformer
BibRef
Alhammad, S.[Sarah],
Zhang, T.[Teng],
Zhao, K.[Kun],
Hobson, P.[Peter],
Jennings, A.[Anthony],
Lovell, B.C.[Brian C.],
Efficient Cell Labelling for Gram Stain WSIs,
ICPR22(4226-4233)
IEEE DOI
2212
Training, Pathology, Annotations, Scholarships, Manuals, Detectors,
Transformers, WSI, Gram Stain Analysis, Detection, CNN, Cell Counting,
Microbiology
BibRef
Launet, L.[Laëtitia],
Colomer, A.[Adrián],
Mosquera-Zamudio, A.[Andrés],
Moscardó, A.[Anaďs],
Monteagudo, C.[Carlos],
Naranjo, V.[Valery],
A Self-Training Weakly-Supervised Framework for Pathologist-Like
Histopathological Image Analysis,
ICIP22(3401-3405)
IEEE DOI
2211
Training, Pathology, Image analysis, Annotations,
Biological system modeling, Data models, Skin, self-training,
whole slide images
BibRef
Si, Y.X.[Yu-Xuan],
Fang, Z.Q.[Zheng-Qing],
Kuang, K.[Kun],
Huang, Z.X.[Zheng-Xing],
Yao, Y.F.[Yu-Feng],
Wu, F.[Fei],
Disentangled Sequential Autoencoder with Local Consistency for
Infectious Keratitis Diagnosis,
ICIP22(3893-3897)
IEEE DOI
2211
Deep learning, Pathology, Pathogens, Shape, Visual impairment,
Time series analysis, Inference algorithms,
Infectious Keratitis
BibRef
Lotfollahi, M.[Mahsa],
Tran, N.[Nguyen],
Gajjela, C.[Chalapathi],
Berisha, S.[Sebastian],
Han, Z.[Zhu],
Mayerich, D.[David],
Reddy, R.[Rohith],
Adaptive Compressive Sampling for Mid-Infrared Spectroscopic Imaging,
ICIP22(2336-2340)
IEEE DOI
2211
Measurement, Tensors, Image coding, Image synthesis, Histopathology,
Training data, Spatial resolution, Adaptive Sampling,
SVM Classification Metric
BibRef
Dwivedi, C.[Chaitanya],
Nofallah, S.[Shima],
Pouryahya, M.[Maryam],
Iyer, J.[Janani],
Leidal, K.[Kenneth],
Chung, C.H.[Chu-Han],
Watkins, T.[Timothy],
Billin, A.[Andrew],
Myers, R.[Robert],
Abel, J.[John],
Behrooz, A.[Ali],
Multi stain graph fusion for multimodal integration in pathology,
CVMI22(1834-1844)
IEEE DOI
2210
Weight measurement, Histopathology, Computational modeling,
Conferences, Liver, Predictive models
BibRef
Alali, M.H.[Mohammed H.],
Roohi, A.[Arman],
Deogun, J.S.[Jitender S.],
Enabling Efficient Training of Convolutional Neural Networks for
Histopathology Images,
DeepHealth22(533-544).
Springer DOI
2208
BibRef
Gräbel, P.[Philipp],
Thull, J.[Julian],
Crysandt, M.[Martina],
Klinkhammer, B.M.[Barbara M.],
Boor, P.[Peter],
Brümmendorf, T.H.[Tim H.],
Merhof, D.[Dorit],
Spatial Maturity Regression for the Classification of Hematopoietic
Cells,
IPTA22(1-6)
IEEE DOI
2206
Visualization, Microscopy, Image processing, Neural networks,
Cells (biology), Bones, Blood, representation learning,
em-bedding guides
BibRef
Azizi, S.[Shekoofeh],
Mustafa, B.[Basil],
Ryan, F.[Fiona],
Beaver, Z.[Zachary],
Freyberg, J.[Jan],
Deaton, J.[Jonathan],
Loh, A.[Aaron],
Karthikesalingam, A.[Alan],
Kornblith, S.[Simon],
Chen, T.[Ting],
Natarajan, V.[Vivek],
Norouzi, M.[Mohammad],
Big Self-Supervised Models Advance Medical Image Classification,
ICCV21(3458-3468)
IEEE DOI
2203
Pathology, Image recognition, Annotations, Dermatology,
Digital cameras, Task analysis, Medical, biological,
BibRef
Abousamra, S.[Shahira],
Belinsky, D.[David],
van Arnam, J.[John],
Allard, F.[Felicia],
Yee, E.[Eric],
Gupta, R.[Rajarsi],
Kurc, T.[Tahsin],
Samaras, D.[Dimitris],
Saltz, J.[Joel],
Chen, C.[Chao],
Multi-Class Cell Detection Using Spatial Context Representation,
ICCV21(3985-3994)
IEEE DOI
2203
Representation learning, Multiplexing, Pathology,
Clustering methods, Topology, Task analysis, Medical, biological,
BibRef
Wang, H.T.[Hao-Tian],
Xian, M.[Min],
Vakanski, A.[Aleksandar],
TA-Net: Topology-Aware Network for Gland Segmentation,
WACV22(3241-3249)
IEEE DOI
2202
Image segmentation, Network topology, Histopathology, Semantics,
Glands, Morphology, Computer architecture, Grouping and Shape
BibRef
Sahel, Y.B.[Yair Ben],
Dardikman-Yoffe, G.[Gilli],
Eldar, Y.C.[Yonina C.],
Gosh, S.[Shirsendu],
Haran, G.[Gilad],
Super-Resolved Imaging of Early-Stage Dynamics in the Immune Response,
ICIP21(3468-3472)
IEEE DOI
2201
Location awareness, Surface reconstruction, Diffraction,
Microscopy, Superresolution, Real-time systems, Surface topography,
High-Resolution Imaging
BibRef
Alhammad, S.[Sarah],
Zhao, K.[Kun],
Jennings, A.[Anthony],
Hobson, P.[Peter],
Smith, D.F.[Daniel F.],
Baker, B.[Brett],
Staweno, J.[Justin],
Lovell, B.C.[Brian C.],
Efficient DNN-Based Classification of Whole Slide Gram Stain Images
for Microbiology,
DICTA21(01-08)
IEEE DOI
2201
Training, Deep learning, Pathology, Microorganisms, Protocols, Oils,
Microscopy, Bacteria Classification, DNN, Computer Aided Diagnosis,
Digital Pathology
BibRef
Shen, Y.Q.[Yi-Qing],
Ke, J.[Jing],
Su-Sampling Based Active Learning for Large-Scale Histopathology
Image,
ICIP21(116-120)
IEEE DOI
2201
Deep learning, Image segmentation, Uncertainty,
Monte Carlo methods, Annotations, Histopathology, Neural networks,
convolutional neural network
BibRef
Dodballapur, V.[Veena],
Song, Y.[Yang],
Huang, H.[Heng],
Chen, M.[Mei],
Chrzanowski, W.[Wojciech],
Cai, W.D.[Wei-Dong],
Dual-Stage Domain Adaptive Mitosis Detection for Histopathology
Images,
DICTA20(1-7)
IEEE DOI
2201
Training, Adaptive systems, Histopathology, Neural networks,
Pipelines, Machine learning, Testing, Domain adaptation, mitosis,
convolutional neural networks
BibRef
Gräbel, P.[Philipp],
Crysandt, M.[Martina],
Klinkhammer, B.M.[Barbara M.],
Boor, P.[Peter],
Brümmendorf, T.H.[Tim H.],
Merhof, D.[Dorit],
Guided Representation Learning for the Classification of
Hematopoietic Cells,
CDPath21(545-551)
IEEE DOI
2112
Training, Dimensionality reduction, Image analysis, Microscopy,
Knowledge based systems, Throughput
BibRef
Pahwa, E.[Esha],
Mehta, D.[Dwij],
Kapadia, S.[Sanjeet],
Jain, D.[Devansh],
Luthra, A.[Achleshwar],
MedSkip: Medical Report Generation Using Skip Connections and
Integrated Attention,
CVAMD21(3402-3408)
IEEE DOI
2112
Visualization, Pathology, Computer architecture,
Radiology, Transformers, Feature extraction
BibRef
Dawood, M.[Muhammad],
Branson, K.[Kim],
Rajpoot, N.M.[Nasir M.],
Minhas, F.U.A.A.[Fayyaz Ul Amir Afsar],
ALBRT: Cellular Composition Prediction in Routine Histology Images,
CDPath21(664-673)
IEEE DOI
2112
Codes, Histopathology, Topology, Task analysis, Tumors
BibRef
Jahanifar, M.[Mostafa],
Tajeddin, N.Z.[Neda Zamani],
Koohbanani, N.A.[Navid Alemi],
Rajpoot, N.[Nasir],
Robust Interactive Semantic Segmentation of Pathology Images with
Minimal User Input,
CDPath21(674-683)
IEEE DOI
2112
Geometry, Deep learning, Image segmentation, Histopathology,
Annotations, Computational modeling, Semantics
BibRef
Jewsbury, R.[Robert],
Bhalerao, A.[Abhir],
Rajpoot, N.[Nasir],
A QuadTree Image Representation for Computational Pathology,
CDPath21(648-656)
IEEE DOI
2112
Visualization, Histopathology, Pipelines,
Data visualization, Image representation, Prediction algorithms
BibRef
Boyd, J.[Joseph],
Liashuha, M.[Mykola],
Deutsch, E.[Eric],
Paragios, N.[Nikos],
Christodoulidis, S.[Stergios],
Vakalopoulou, M.[Maria],
Self-Supervised Representation Learning using Visual Field Expansion
on Digital Pathology,
CDPath21(639-647)
IEEE DOI
2112
Visualization, Codes, Histopathology,
Computational modeling, Tools
BibRef
Lai, Z.F.[Zheng-Feng],
Wang, C.[Chao],
Oliveira, L.C.[Luca Cerny],
Dugger, B.N.[Brittany N.],
Cheung, S.C.[Sen-Ching],
Chuah, C.N.[Chen-Nee],
Joint Semi-supervised and Active Learning for Segmentation of
Gigapixel Pathology Images with Cost-Effective Labeling,
CDPath21(591-600)
IEEE DOI
2112
Training, Deep learning, Pathology, Image segmentation,
Image analysis, Manuals
BibRef
Marini, N.[Niccolň],
Atzori, M.[Manfredo],
Otálora, S.[Sebastian],
Marchand-Maillet, S.[Stephane],
Müller, H.[Henning],
H&E-adversarial network: a convolutional neural network to learn
stain-invariant features through Hematoxylin & Eosin regression,
CDPath21(601-610)
IEEE DOI
2112
Training, Image segmentation, Image color analysis, Histopathology,
Neural networks, Convolutional neural networks
BibRef
Weitz, P.[Philippe],
Wang, Y.[Yinxi],
Hartman, J.[Johan],
Rantalainen, M.[Mattias],
An investigation of attention mechanisms in histopathology
whole-slide-image analysis for regression objectives,
CDPath21(611-619)
IEEE DOI
2112
Analytical models, Histopathology,
Computational modeling, Focusing, Predictive models
BibRef
Deuschel, J.[Jessica],
Firmbach, D.[Daniel],
Geppert, C.I.[Carol I.],
Eckstein, M.[Markus],
Hartmann, A.[Arndt],
Bruns, V.[Volker],
Kuritcyn, P.[Petr],
Dexl, J.[Jakob],
Hartmann, D.[David],
Perrin, D.[Dominik],
Wittenberg, T.[Thomas],
Benz, M.[Michaela],
Multi-Prototype Few-shot Learning in Histopathology,
CDPath21(620-628)
IEEE DOI
2112
Training, Degradation, Histopathology, Neural networks,
Prototypes, Distributed databases
BibRef
Srinidhi, C.L.[Chetan L.],
Martel, A.L.[Anne L.],
Improving Self-supervised Learning with Hardness-aware Dynamic
Curriculum Learning: An Application to Digital Pathology,
CDPath21(562-571)
IEEE DOI
2112
Training, Visualization, Histopathology, Annotations,
Benchmark testing, Robustness, Complexity theory
BibRef
Tang, S.[Sheyang],
Hosseini, M.S.[Mahdi S.],
Chen, L.[Lina],
Varma, S.[Sonal],
Rowsell, C.[Corwyn],
Damaskinos, S.[Savvas],
Plataniotis, K.N.[Konstantinos N.],
Wang, Z.[Zhou],
Probeable DARTS with Application to Computational Pathology,
CDPath21(572-581)
IEEE DOI
2112
Measurement, Knowledge engineering, Pathology,
Computer network reliability, Robustness
BibRef
Gamper, J.[Jevgenij],
Rajpoot, N.[Nasir],
Multiple Instance Captioning: Learning Representations from
Histopathology Textbooks and Articles,
CVPR21(16544-16554)
IEEE DOI
2111
Histopathology, Computational modeling,
Estimation, Pattern recognition, Task analysis
BibRef
Zhang, J.W.[Jing-Wei],
Ma, K.[Ke],
van Arnam, J.[John],
Gupta, R.[Rajarsi],
Saltz, J.[Joel],
Vakalopoulou, M.[Maria],
Samaras, D.[Dimitris],
A Joint Spatial and Magnification Based Attention Framework for Large
Scale Histopathology Classification,
CVMI21(3771-3779)
IEEE DOI
2109
Training, Deep learning, Histopathology, Microscopy, Tools,
Probability distribution, Pattern recognition
BibRef
tepec, D.[Dejan],
Skocaj, D.[Danijel],
Unsupervised Detection of Cancerous Regions in Histology Imagery
using Image-to-Image Translation,
CVMI21(3780-3787)
IEEE DOI
2109
Visualization, Image analysis, Histopathology,
Biomedical measurement, Pattern recognition
BibRef
Wei, J.[Jerry],
Suriawinata, A.[Arief],
Ren, B.[Bing],
Liu, X.Y.[Xiao-Ying],
Lisovsky, M.[Mikhail],
Vaickus, L.[Louis],
Brown, C.[Charles],
Baker, M.[Michael],
Nasir-Moin, M.[Mustafa],
Tomita, N.[Naofumi],
Torresani, L.[Lorenzo],
Wei, J.[Jason],
Hassanpour, S.[Saeed],
Learn like a Pathologist: Curriculum Learning by Annotator Agreement
for Histopathology Image Classification,
WACV21(2472-2482)
IEEE DOI
2106
Training, Learning systems, Histopathology,
Task analysis, Image classification
BibRef
Belharbi, S.[Soufiane],
Ben Ayed, I.[Ismail],
McCaffrey, L.[Luke],
Granger, E.[Eric],
Deep Active Learning for Joint Classification Segmentation with Weak
Annotator,
WACV21(3337-3346)
IEEE DOI
2106
Training, Image segmentation, Visualization, Protocols, Annotations,
Histopathology, Training data
BibRef
Gong, X.[Xuan],
Chen, S.Y.[Shu-Yan],
Zhang, B.C.[Bao-Chang],
Doermann, D.[David],
Style Consistent Image Generation for Nuclei Instance Segmentation,
WACV21(3993-4002)
IEEE DOI
2106
Training, Image segmentation, Image analysis, Histopathology, Shape,
Image synthesis, Pipelines
BibRef
Zhao, S.[Shuai],
Li, X.[Xuanya],
Chen, Z.N.[Zhi-Neng],
Liu, C.[Chang],
Peng, C.G.[Chang-Gen],
Res2-unet: An Enhanced Network for Generalized Nuclear Segmentation in
Pathological Images,
MMMod21(II:87-98).
Springer DOI
2106
BibRef
Luo, J.Q.[Jia-Qi],
Zhao, Z.C.[Zhi-Cheng],
Su, F.[Fei],
Guo, L.[Limei],
Triplet-path Dilated Network for Detection and Segmentation of
General Pathological Images,
ICPR21(1452-1459)
IEEE DOI
2105
Image segmentation, Pathology, Visualization,
Object detection, Feature extraction, Robustness
BibRef
Yao, Z.Y.[Ze-Yi],
Li, K.Q.[Kai-Qi],
Luo, Y.[Yiwen],
Zhou, X.G.[Xiao-Guang],
Sun, M.[Muyi],
Zhang, G.H.[Guan-Hong],
Accurate Cell Segmentation in Digital Pathology Images via Attention
Enforced Networks,
ICPR21(1590-1595)
IEEE DOI
2105
Pathology, Image segmentation, Solid modeling, Design automation,
Image color analysis, Pipelines, Prediction algorithms,
digital pathology images
BibRef
Shin, B.[Beomjo],
Cho, J.[Junsu],
Yu, H.[Hwanjo],
Choi, S.J.[Seung-Jin],
Sparse Network Inversion for Key Instance Detection in Multiple
Instance Learning,
ICPR21(4083-4090)
IEEE DOI
2105
Training, Gradient methods, Histopathology, Neural networks,
Predictive models, Pattern recognition, Numerical models
BibRef
Ozen, Y.[Yigit],
Aksoy, S.[Selim],
Kösemehmetoglu, K.[Kemal],
Önder, S.[Sevgen],
Üner, A.[Aysegül],
Self-Supervised Learning with Graph Neural Networks for Region of
Interest Retrieval in Histopathology,
ICPR21(6329-6334)
IEEE DOI
2105
Training, Learning systems, Histopathology, Shape, Transfer learning,
Image retrieval, Breast, Digital pathology,
content-based image retrieval
BibRef
Sikaroudi, M.[Milad],
Ghojogh, B.[Benyamin],
Karray, F.[Fakhri],
Crowley, M.[Mark],
Tizhoosh, H.R.,
Batch-Incremental Triplet Sampling for Training Triplet Networks
Using Bayesian Updating Theorem,
ICPR21(7080-7086)
IEEE DOI
2105
Training, Histopathology, Training data, Stochastic processes,
Gaussian distribution, Bayes methods, Data mining
BibRef
Bussola, N.[Nicole],
Marcolini, A.[Alessia],
Maggio, V.[Valerio],
Jurman, G.[Giuseppe],
Furlanello, C.[Cesare],
AI Slipping on Tiles: Data Leakage in Digital Pathology,
AIDP20(167-182).
Springer DOI
2103
Reproducible results.
BibRef
Sikaroudi, M.[Milad],
Ghojogh, B.[Benyamin],
Safarpoor, A.[Amir],
Karray, F.[Fakhri],
Crowley, M.[Mark],
Tizhoosh, H.R.[Hamid R.],
Offline Versus Online Triplet Mining Based on Extreme Distances of
Histopathology Patches,
ISVC20(I:333-345).
Springer DOI
2103
BibRef
Maleki, D.[Danial],
Afshari, M.[Mehdi],
Babaie, M.[Morteza],
Tizhoosh, H.R.,
Ink Marker Segmentation in Histopathology Images Using Deep Learning,
ISVC20(I:359-368).
Springer DOI
2103
BibRef
Cheng, H.T.[Hsien-Tzu],
Yeh, C.F.[Chun-Fu],
Kuo, P.C.[Po-Chen],
Wei, A.[Andy],
Liu, K.C.[Keng-Chi],
Ko, M.C.[Mong-Chi],
Chao, K.H.[Kuan-Hua],
Peng, Y.C.[Yu-Ching],
Liu, T.L.[Tyng-Luh],
Self-similarity Student for Partial Label Histopathology Image
Segmentation,
ECCV20(XXV:117-132).
Springer DOI
2011
BibRef
Xiang, Y.,
Chen, J.,
Liu, Q.,
Liang, Y.,
Disentangled Representation Learning Based Multidomain Stain
Normalization For Histological Images,
ICIP20(360-364)
IEEE DOI
2011
Image color analysis, Image reconstruction,
Generative adversarial networks, Training, Decoding, Generators,
Deep Learning
BibRef
Hosseini, M.S.[Mahdi S.],
Chan, L.[Lyndon],
Huang, W.M.[Wei-Min],
Wang, Y.C.[Yi-Chen],
Hasan, D.[Danial],
Rowsell, C.[Corwyn],
Damaskinos, S.[Savvas],
Plataniotis, K.N.[Konstantinos N.],
On Transferability of Histological Tissue Labels in Computational
Pathology,
ECCV20(XXIX: 453-469).
Springer DOI
2010
BibRef
Cheeseman, A.K.[Alison K.],
Tizhoosh, H.R.[Hamid R.],
Vrscay, E.R.[Edward R.],
Studying the Effect of Digital Stain Separation of Histopathology
Images on Image Search Performance,
ICIAR20(II:262-273).
Springer DOI
2007
BibRef
Alinsaif, S.,
Lang, J.,
Histological Image Classification using Deep Features and Transfer
Learning,
CRV20(101-108)
IEEE DOI
2006
Deep learning, Fine-tuning, CNN-Based Features, histopathological,
SVM, classification
BibRef
Hosseini, M.S.[Mahdi S.],
Chan, L.[Lyndon],
Tse, G.[Gabriel],
Tang, M.[Michael],
Deng, J.[Jun],
Norouzi, S.[Sajad],
Rowsell, C.[Corwyn],
Plataniotis, K.N.[Konstantinos N.],
Damaskinos, S.[Savvas],
Atlas of Digital Pathology: A Generalized Hierarchical Histological
Tissue Type-Annotated Database for Deep Learning,
CVPR19(11739-11748).
IEEE DOI
2002
BibRef
Hou, L.[Le],
Agarwal, A.[Ayush],
Samaras, D.[Dimitris],
Kurc, T.M.[Tahsin M.],
Gupta, R.R.[Rajarsi R.],
Saltz, J.H.[Joel H.],
Robust Histopathology Image Analysis: To Label or to Synthesize?,
CVPR19(8525-8534).
IEEE DOI
2002
BibRef
Cheeseman, A.K.[Alison K.],
Tizhoosh, H.[Hamid],
Vrscay, E.R.[Edward R.],
A Compact Representation of Histopathology Images Using Digital Stain
Separation and Frequency-Based Encoded Local Projections,
ICIAR19(II:147-158).
Springer DOI
1909
BibRef
Stanisavljevic, M.[Milos],
Anghel, A.[Andreea],
Papandreou, N.[Nikolaos],
Andani, S.[Sonali],
Pati, P.[Pushpak],
Rüschoff, J.H.[Jan Hendrik],
Wild, P.[Peter],
Gabrani, M.[Maria],
Pozidis, H.[Haralampos],
A Fast and Scalable Pipeline for Stain Normalization of Whole-Slide
Images in Histopathology,
BioIm18(VI:424-436).
Springer DOI
1905
BibRef
Kieffer, B.,
Babaie, M.,
Kalra, S.,
Tizhoosh, H.R.,
Convolutional neural networks for histopathology image
classification: Training vs. Using pre-trained networks,
IPTA17(1-6)
IEEE DOI
1804
feature extraction, image classification, image representation,
learning (artificial intelligence), medical image processing,
medical imaging
BibRef
Valkonen, M.,
Kartasalo, K.,
Liimatainen, K.,
Nykter, M.,
Latonen, L.,
Ruusuvuori, P.,
Dual Structured Convolutional Neural Network with Feature
Augmentation for Quantitative Characterization of Tissue Histology,
BioIm17(27-35)
IEEE DOI
1802
Biological system modeling, Feature extraction, Histograms,
Image analysis, Pathology, Training
BibRef
Li, W.,
Qian, X.,
Ji, J.,
Noise-tolerant deep learning for histopathological image segmentation,
ICIP17(3075-3079)
IEEE DOI
1803
Diseases, Image color analysis, Image segmentation,
Machine learning, Muscles, Noise measurement, Training,
noisy labels
BibRef
Astola, L.[Laura],
Stain separation in digital bright field histopathology,
IPTA16(1-6)
IEEE DOI
1703
biological tissues
BibRef
Agarwal, N.[Nitin],
Xu, X.M.[Xiang-Min],
Gopi, M.,
Automatic Detection of Histological Artifacts in Mouse Brain Slice
Images,
MCV16(105-115).
Springer DOI
1711
BibRef
Corredor, G.[German],
Romero, E.[Eduardo],
Learning histopathological regions of interest by fusing bottom-up
and top-down information,
ICIP15(3200-3204)
IEEE DOI
1512
Histopathology
BibRef
Li, X.Y.[Xing-Yu],
Plataniotis, K.N.[Konstantinos N.],
Diagnostic color estimation of tissue components in pathology images
via von Mises mixture model,
ICIP15(2060-2064)
IEEE DOI
1512
Pathology image
BibRef
Hatipoglu, N.,
Bilgin, G.,
Classification of histopathological images using convolutional neural
network,
IPTA14(1-6)
IEEE DOI
1503
image classification
BibRef
McCann, M.T.[Michael T.],
Majumdar, J.[Joshita],
Peng, C.[Cheng],
Castro, C.A.[Carlos A.],
Kovacevic, J.[Jelena],
Algorithm and benchmark dataset for stain separation in histology
images,
ICIP14(3953-3957)
IEEE DOI
1502
Accuracy
BibRef
Sommer, C.[Christoph],
Fiaschi, L.[Luca],
Hamprecht, F.A.[Fred A.],
Gerlich, D.W.[Daniel W.],
Learning-based mitotic cell detection in histopathological images,
ICPR12(2306-2309).
WWW Link.
1302
BibRef
Toutain, M.,
Lézoray, O.,
Audigié, F.,
Busoni, V.,
Rossi, G.,
Parillo, F.,
El Moataz, A.,
Analysis of Whole Slide Images of Equine Tendinopathy,
ICIAR12(II: 440-447).
Springer DOI
1206
BibRef
Díaz, G.[Gloria],
Romero, E.[Eduardo],
Histopathological Image Classification Using Stain Component Features
on a pLSA Model,
CIARP10(55-62).
Springer DOI
1011
BibRef
Cooper, L.[Lee],
Saltz, J.[Joel],
Machiraju, R.[Raghu],
Huang, K.[Kun],
Two-point correlation as a feature for histology images:
Feature space structure and correlation updating,
MMBIA10(79-86).
IEEE DOI
1006
BibRef
Graf, F.[Felix],
Grzegorzek, M.[Marcin],
Paulus, D.[Dietrich],
Counting Lymphocytes in Histopathology Images Using Connected
Components,
ICPR-Contests10(263-269).
Springer DOI
1008
BibRef
Cheng, J.[Jierong],
Veronika, M.[Merlin],
Rajapakse, J.C.[Jagath C.],
Identifying Cells in Histopathological Images,
ICPR-Contests10(244-252).
Springer DOI
1008
BibRef
Kuse, M.[Manohar],
Sharma, T.[Tanuj],
Gupta, S.[Sudhir],
A Classification Scheme for Lymphocyte Segmentation in H&E Stained
Histology Images,
ICPR-Contests10(235-243).
Springer DOI
1008
BibRef
Gurcan, M.N.[Metin N.],
Madabhushi, A.[Anant],
Rajpoot, N.[Nasir],
Pattern Recognition in Histopathological Images: An ICPR 2010 Contest,
ICPR-Contests10(226-234).
Springer DOI
1008
BibRef
Thomas, K.A.[Kristine A.],
Sottile, M.J.[Matthew J.],
Salafia, C.M.[Carolyn M.],
Unsupervised Segmentation for Inflammation Detection in Histopathology
Images,
ICISP10(541-549).
Springer DOI
1006
BibRef
Noah, S.A.[Shahrul Azman],
Yaakob, S.[Suraya],
Shahar, S.[Suzana],
Application of Information Visualization Techniques in Representing
Patients' Temporal Personal History Data,
IVIC09(168-179).
Springer DOI
0911
BibRef
Cosatto, E.[Eric],
Miller, M.[Matt],
Graf, H.P.[Hans Peter],
Meyer, J.S.[John S.],
Grading nuclear pleomorphism on histological micrographs,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Canada, B.A.[Brian A.],
Thomas, G.K.[Georgia K.],
Cheng, K.C.[Keith C.],
Wang, J.Z.[James Z.],
Liu, Y.X.[Yan-Xi],
Automatic lattice detection in near-regular histology array images,
ICIP08(1452-1455).
IEEE DOI
0810
BibRef
And:
Towards efficient automated characterization of irregular histology
images via transformation to frieze-like patterns,
CIVR08(581-590).
0807
BibRef
Zhao, D.H.[De-Hua],
Chen, Y.X.[Yi-Xin],
Correa, H.,
Statistical Categorization of Human Histological Images,
ICIP05(III: 628-631).
IEEE DOI
0512
BibRef
Roula, M.A.,
Bouridane, A.,
Kurugollu, F.,
An evolutionary snake algorithm for the segmentation of nuclei in
histopathological images,
ICIP04(I: 127-130).
IEEE DOI
0505
BibRef
Nedzved, A.,
Ablameyko, S.V.,
Pitas, I.,
Morphological Segmentation of Histology Cell Images,
ICPR00(Vol I: 500-503).
IEEE DOI
0009
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Fluorescence Analysis, Microscopic Analysis, Cells .