21.4.4.5 Histopathology, Tissue Analysis

Chapter Contents (Back)
Histopathology. Pathology. A subset:
See also Whole Slide Analysis, Histopahtology, Cells.

Stain Normalization toolbox for histopathology image analysis,
OnlineOctober 2014.
WWW Link. Code, Medical Analysis. 1410
MATLAB implementation of well-known stain normalization algorithms. BibRef

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

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

Bentaieb, A., Hamarneh, G.,
Adversarial Stain Transfer for Histopathology Image Analysis,
MedImg(37), No. 3, March 2018, pp. 792-802.
IEEE DOI 1804
biological tissues, biomedical optical imaging, cancer, feature extraction, image classification, image colour analysis, stain normalization BibRef

Öztürk, S.[Saban], Akdemir, B.[Bayram],
Cell-type based semantic segmentation of histopathological images using deep convolutional neural networks,
IJIST(29), No. 3, September 2019, pp. 234-246.
DOI Link 1908
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

Serin, F.[Faruk], Erturkler, M.[Metin],
A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing,
IJIST(30), No. 2, 2020, pp. 311-326.
DOI Link 2005
cell graph, histopathological image analyzing, nuclei, proximity graph 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

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

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

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.Y.[Zhi-Ying], 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

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

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

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

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

Wang, H.X.[Hong-Xiao], Huang, G.[Gang], Zhao, Z.[Zhuo], Cheng, L.[Liang], Juncker-Jensen, A.[Anna], Nagy, M.L.[Máté Levente], Lu, X.[Xin], Zhang, X.L.[Xiang-Liang], Chen, D.Z.[Danny Z.],
CCF-GNN: A Unified Model Aggregating Appearance, Microenvironment, and Topology for Pathology Image Classification,
MedImg(42), No. 11, November 2023, pp. 3179-3193.
IEEE DOI 2311
BibRef

Guo, Y.F.[Yi-Fei], Hu, M.[Menghan], Min, X.K.[Xiong-Kuo], Wang, Y.[Yan], Dai, M.[Min], Zhai, G.T.[Guang-Tao], Zhang, X.P.[Xiao-Ping], Yang, X.K.[Xiao-Kang],
Blind Image Quality Assessment for Pathological Microscopic Image Under Screen and Immersion Scenarios,
MedImg(42), No. 11, November 2023, pp. 3295-3306.
IEEE DOI Code:
WWW Link. 2311
BibRef

Ling, Y.[Yu], Tan, W.M.[Wei-Min], Yan, B.[Bo],
Self-Supervised Digital Histopathology Image Disentanglement for Arbitrary Domain Stain Transfer,
MedImg(42), No. 12, December 2023, pp. 3625-3638.
IEEE DOI 2312
BibRef

Ke, J.[Jing], Liu, K.[Kai], Sun, Y.X.[Yu-Xiang], Xue, Y.Y.[Yu-Ying], Huang, J.X.[Jia-Xuan], Lu, Y.Z.[Yi-Zhou], Dai, J.[Jun], Chen, Y.[Yaobing], Han, X.D.[Xiao-Dan], Shen, Y.Q.[Yi-Qing], Shen, D.G.[Ding-Gang],
Artifact Detection and Restoration in Histology Images With Stain-Style and Structural Preservation,
MedImg(42), No. 12, December 2023, pp. 3487-3500.
IEEE DOI Code:
WWW Link. 2312
BibRef

Hassan, T.[Taimur], Li, Z.[Zhu], Javed, S.[Sajid], Dias, J.[Jorge], Werghi, N.[Naoufel],
Neural Graph Refinement for Robust Recognition of Nuclei Communities in Histopathological Landscape,
IP(33), 2024, pp. 241-256.
IEEE DOI 2312
BibRef

Lagogiannis, I.[Ioannis], Meissen, F.[Felix], Kaissis, G.[Georgios], Rueckert, D.[Daniel],
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art,
MedImg(43), No. 1, January 2024, pp. 241-252.
IEEE DOI Code:
WWW Link. 2401
BibRef

He, K.[Keke], Zhu, J.[Jun], Li, L.[Limiao], Gou, F.F.[Fang-Fang], Wu, J.[Jia],
Two-stage coarse-to-fine method for pathological images in medical decision-making systems,
IET-IPR(18), No. 1, 2024, pp. 175-193.
DOI Link 2401
cell recognition, computer-aided diagnosis, medical decision-making system, pathological images, segmentation and refinement BibRef

Rao, K.[Karishma], Bansal, M.[Manu], Kaur, G.[Gagandeep],
An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain,
IJIST(34), No. 1, 2024, pp. e22982.
DOI Link 2401
gamma function, image enhancement, particle swarm optimization, singular value equalization BibRef

He, Q.M.[Qi-Ming], Zeng, S.Q.[Si-Qi], Ge, S.[Shuang], Wang, Y.X.[Yan-Xia], Ye, J.[Jing], He, Y.H.[Yong-Hong], Guan, T.[Tian], Wang, Z.[Zhe], Li, J.[Jing],
Identifying and matching 12-level multistained glomeruli via deep learning for diagnosis of glomerular diseases,
IJIST(34), No. 2, 2024, pp. e23032.
DOI Link 2402
glomerular diseases, instance segmentation, matching algorithm, deep learning, histopathology BibRef

Hu, W.T.[Wen-Tao], Cheng, L.[Lianglun], Huang, G.[Guoheng], Yuan, X.C.[Xiao-Chen], Zhong, G.[Guo], Pun, C.M.[Chi-Man], Zhou, J.[Jian], Cai, M.[Muyan],
Learning From Incorrectness: Active Learning With Negative Pre-Training and Curriculum Querying for Histological Tissue Classification,
MedImg(43), No. 2, February 2024, pp. 625-637.
IEEE DOI Code:
WWW Link. 2402
Annotations, Training, Data models, Cancer, Predictive models, Uncertainty, Costs, Active learning, negative learning, histological tissue classification BibRef

Zhang, Y.M.[Yuan-Ming], Li, Z.[Zheng], Han, X.M.[Xiang-Min], Ding, S.S.[Sai-Sai], Li, J.C.[Jun-Cheng], Wang, J.[Jun], Ying, S.H.[Shi-Hui], Shi, J.[Jun],
Pseudo-Data Based Self-Supervised Federated Learning for Classification of Histopathological Images,
MedImg(43), No. 3, March 2024, pp. 902-915.
IEEE DOI 2403
Solid modeling, Data models, Training, Task analysis, Moon, Multitasking, Hospitals, Histopathological image, Barlow twins contrastive learning BibRef


Nakhli, R.[Ramin], Zhang, A.[Allen], Mirabadi, A.[Ali], Rich, K.[Katherine], Asadi, M.[Maryam], Gilks, B.[Blake], Farahani, H.[Hossein], Bashashati, A.[Ali],
CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neighborhood Aggregation for Multi-Gigapixel Histopathology Image Representation,
ICCV23(21006-21016)
IEEE DOI 2401
BibRef

Jiménez, L.G.[Laura Gálvez], Dierckx, L.[Lucile], Amodei, M.[Maxime], Khosroshahi, H.R.[Hamed Razavi], Chidambaran, N.[Natarajan], Ho, A.T.P.[Anh-Thu Phan], Franzin, A.[Alberto],
Computational Evaluation of the Combination of Semi-Supervised and Active Learning for Histopathology Image Segmentation with Missing Annotations,
CVAMD23(2544-2555)
IEEE DOI 2401
BibRef

Sikaroudi, M.[Milad], Hosseini, M.[Maryam], Rahnamayan, S.[Shahryar], Tizhoosh, H.R.,
ALFA: Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals,
CVAMD23(2656-2665)
IEEE DOI Code:
WWW Link. 2401
BibRef

Wu, W.Y.[Wei-Yi], Gao, C.Y.[Chong-Yang], DiPalma, J.[Joseph], Vosoughi, S.[Soroush], Hassanpour, S.[Saeed],
Improving Representation Learning for Histopathologic Images with Cluster Constraints,
ICCV23(21347-21357)
IEEE DOI Code:
WWW Link. 2401
BibRef

Lee, J.C.[Ju Cheon], Kwak, J.T.[Jin Tae],
Order-ViT: Order Learning Vision Transformer for Cancer Classification in Pathology Images,
CVAMD23(2485-2494)
IEEE DOI 2401
BibRef

Lai, Z.F.[Zheng-Feng], Li, Z.H.[Zhuo-Heng], Oliveira, L.C.[Luca Cerny], Chauhan, J.[Joohi], Dugger, B.N.[Brittany N.], Chuah, C.N.[Chen-Nee],
CLIPath: Fine-tune CLIP with Visual Feature Fusion for Pathology Image Analysis Towards Minimizing Data Collection Efforts,
CVAMD23(2366-2372)
IEEE DOI 2401
BibRef

Ryu, J.[Jeongun], Puche, A.V.[Aaron Valero], Shin, J.W.[Jae-Woong], Park, S.[Seonwook], Brattoli, B.[Biagio], Lee, J.[Jinhee], Jung, W.[Wonkyung], Cho, S.I.[Soo Ick], Paeng, K.[Kyunghyun], Ock, C.Y.[Chan-Young], Yoo, D.[Donggeun], Pereira, S.[Sérgio],
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology,
CVPR23(23902-23912)
IEEE DOI 2309
BibRef

Zedda, L.[Luca], Loddo, A.[Andrea], di Ruberto, C.[Cecilia],
Hierarchical Pretrained Backbone Vision Transformer for Image Classification in Histopathology,
CIAP23(II:223-234).
Springer DOI 2312
BibRef

Bontempo, G.[Gianpaolo], Bartolini, N.[Nicola], Lovino, M.[Marta], Bolelli, F.[Federico], Virtanen, A.[Anni], Ficarra, E.[Elisa],
Enhancing PFI Prediction with GDS-MIL: A Graph-based Dual Stream MIL Approach,
CIAP23(I:550-562).
Springer DOI 2312
BibRef

Nau, A.M.[Anna-Maria], Mockus, A.[Audris], Steadman, D.W.[Dawnie Wolfe],
Stage of Decay Estimation Exploiting Exogenous and Endogenous Image Attributes to Minimize Manual Labeling Efforts and Maximize Classification Performance,
ICIP23(2705-2709)
IEEE DOI 2312
For human remains. BibRef

Long, X.[Xi], Liu, J.X.[Jing-Xin], Hou, X.X.[Xian-Xu],
Domain Adaptation of Digital Pathology Images using Joint Stain Color and Image Quality Constraints,
ICIP23(1805-1809)
IEEE DOI 2312
BibRef

Tan, C.H.[Choo Hui], Lim, W.J.[Wei Jie], Ahmad, W.S.H.M.W.[Wan Siti Halimatul Munirah Wan], Wong, L.K.[Lai-Kuan], Rehman, Z.U.[Zaka Ur], Looi, L.M.[Lai Meng], Cheah, P.L.[Phaik Leng], Toh, Y.F.[Yen Fa], Fauzi, M.F.A.[Mohammad Faizal Ahmad],
HER2-Sish Histopathology Image Classification Using Deep Neural Networks,
ICIP23(2500-2504)
IEEE DOI 2312
BibRef

Sun, K.[Kexin], Chen, Z.[Zhineng], Wang, G.[Gongwei], Liu, J.[Jun], Ye, X.J.[Xiong-Jun], Jiang, Y.G.[Yu-Gang],
Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-Staining,
CVPR23(3904-3913)
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

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

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

Li, M.[Meng], Zhao, K.[Kun], Peng, C.[Can], Hobson, P.[Peter], Jennings, T.[Tony], Lovell, B.C.[Brian C.],
Deep Adaptive Few Example Learning for Microscopy Image Cell Counting,
DICTA21(1-7)
IEEE DOI 2201
Training, Deep learning, Adaptation models, Adaptive systems, Histopathology, Microscopy, Digital images, Few-shot Learning 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

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

Štepec, 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
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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

Xu, G., Song, Z., Sun, Z., Ku, C., Yang, Z., Liu, C., Wang, S., Ma, J., Xu, W.,
CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation,
ICCV19(10681-10690)
IEEE DOI 2004
cancer, image classification, image segmentation, learning (artificial intelligence), medical image processing, Feature extraction BibRef

Bidart, R.[Rene], Wong, A.[Alexander],
TriResNet: A Deep Triple-Stream Residual Network for Histopathology Grading,
ICIAR19(II:369-382).
Springer DOI 1909
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
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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
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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).
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Díaz, G.[Gloria], Romero, E.[Eduardo],
Histopathological Image Classification Using Stain Component Features on a pLSA Model,
CIARP10(55-62).
Springer DOI 1011
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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
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Graf, F.[Felix], Grzegorzek, M.[Marcin], Paulus, D.[Dietrich],
Counting Lymphocytes in Histopathology Images Using Connected Components,
ICPR-Contests10(263-269).
Springer DOI 1008
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Cheng, J.[Jierong], Veronika, M.[Merlin], Rajapakse, J.C.[Jagath C.],
Identifying Cells in Histopathological Images,
ICPR-Contests10(244-252).
Springer DOI 1008
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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
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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
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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
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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
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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
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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
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And:
Towards efficient automated characterization of irregular histology images via transformation to frieze-like patterns,
CIVR08(581-590). 0807
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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
Whole Slide Analysis, Histopahtology, Cells .


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