A method for normalizing histology slides for quantitative analysis

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Improving Melanoma Classification by Integrating Genetic and Morphologic Features

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Distance-Weighted Discrimination

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Comparison of Quantification of Histochemical Staining By Hue-Saturation-Intensity (HSI) Transformation and Color-Deconvolution

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Unsupervised Color Decomposition of Histologically Stained Tissue Samples

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A Framework for the Automated Analysis of Subcellular Patterns in Human Protein Atlas Images

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Factors to keep in mind when introducing virtual microscopy

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Spectral Imaging and Pathology: Seeing More

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Methodological aspects on immunohistochemistry in dermatology with special reference to neuronal markers

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Automatic color unmixing of IHC stained whole slide images

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Stain Style Transfer Using Transitive Adversarial Networks

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Circular Clustering in Fuzzy Approximation Spaces for Color Normalization of Histological Images

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StainCNNs: An efficient stain feature learning method

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Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks

Aloyayri, A., & Krzyżak, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61401-0_45 · Cited by 18

Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma

Arlova, A., Jin, C., Wong-Rolle, A., Chen, E. S., Lisle, C. R., Brown, G. T., Lay, N. S., Choyke, P. L., Türkbey, B., Harmon, S. A., et al. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100007 · Cited by 17

Detecting and characterizing cellular responses to Mycobacterium tuberculosis from histology slides

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Computational method for unsupervised segmentation of lymphoma histological images based on fuzzy 3-partition entropy and genetic algorithm

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Enhancing Histopathological Colorectal Cancer Image Classification by using Convolutional Neural NetworkPreprint

Al.Shawesh, R., & Chen, Y. X. (2021) · medRxiv · doi:10.1101/2021.03.17.21253390 · Cited by 17

Spatiotemporally resolved transcriptomics reveals the cellular dynamics of human retinal development

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Paving the Roadmap for XAI and IML in Healthcare: Data-Driven Discoveries and the FIXAIH Framework

Alghamdi, S., Mehmood, R., Alqurashi, F. A., & Alzahrani, A. (2025) · IEEE Access · doi:10.1109/access.2025.3616353 · Cited by 22

Colour normalisation of histopathological images

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H&E Multi-Laboratory Staining Variance Exploration with Machine Learning

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Fast FF-to-FFPE Whole Slide Image Translation via Laplacian Pyramid and Contrastive Learning

Fan, L., Sowmya, A., Meijering, E. H. W., & Song, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_40 · Cited by 16

Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E‐stained tissues

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OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification

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Computer aided analysis of prostate histopathology images Gleason grading especially for Gleason score 7

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Domain Adaptive Classification for Compensating Variability in Histopathological Whole Slide Images

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H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression

Marini, N., Atzori, M., Otálora, S., Marchand‐Maillet, S., & Müller, H. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00073 · Cited by 16

Tissue Type Recognition in Whole Slide Histological Images

Khvostikov, A. V., Krylov, A. S., Mikhailov, I. А., Malkov, P. G., & Danilova, N. V. (2021) · International Conference on Computer Graphics and Vision (GraphiCon) · doi:10.20948/graphicon-2021-3027-496-507 · Cited by 16

Automated knowledge-assisted mitosis cells detection framework in breast histopathology images

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Computational approach for mitotic cell detection and its application in oral squamous cell carcinoma

Das, D. K., Mitra, P., Chakraborty, C., Chatterjee, S., Maiti, A. K., & Bose, S. (2017) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-017-0488-6 · Cited by 16

Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network

Huang, Y., & Chung, A. C. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00949-6_3 · Cited by 16

Deep Convolutional Gaussian Mixture Model for Stain-Color Normalization of Histopathological Images

Zanjani, F. G., Zinger, S., & de With, P. H. N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_31 · Cited by 16

Recent advances in tissue imaging for cancer research

Maiques, Ó., Georgouli, M., & Sanz‐Moreno, V. (2019) · F1000Research · doi:10.12688/f1000research.19037.1 · Cited by 16

Multidimensional MRI for Characterization of Subtle Axonal Injury Accelerated Using an Adaptive Nonlocal Multispectral Filter

Benjamini, D., Bouhrara, M., Komlosh, M. E., Iacono, D., Perl, D. P., Brody, D. L., & Basser, P. J. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.737374 · Cited by 16

Benchmarking artificial intelligence methods for end-to-end computational pathology

Laleh, N. G., Muti, H. S., Lavinia Loeffler, C. M., Echle, A., Saldanha, O. L., Mahmood, F., Lu, M. Y., Trautwein, C., Langer, R., Dislich, B., et al. (2021) · doi:10.1101/2021.08.09.455633 · Cited by 13

Histopathological image classification based on self-supervised vision transformer and weak labels

Gul, A. G., Çetin, Ö., Reich, C., Flinner, N., Prangemeier, T., & Koeppl, H. (2022) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2624609 · Cited by 16

Breast cancer image analysis using deep learning techniques – a survey

Koshy, S. S., Anbarasi, L. J., Jawahar, M., & Ravi, V. (2022) · Health and Technology · doi:10.1007/s12553-022-00703-5 · Cited by 19

Susceptibility Magnetic Resonance Imaging Correlates with Glial Density and Tau in the Substantia Nigra Pars Compacta

Wang, E. W., Brown, G. L., Lewis, M. M., Jellen, L. C., Pu, C., Johnson, M. L., Chen, H., Kong, L., Du, G., & Huang, X. (2023) · Movement Disorders · doi:10.1002/mds.29311 · Cited by 15

Predicting the HER2 status in oesophageal cancer from tissue microarrays using convolutional neural networks

Pisula, J. I., Datta, R. R., Valdez, L. B., Avemarg, J.-R., Jung, J., Plum, P. S., Löser, H., Lohneis, P., Meuschke, M., dos Santos, D. P., et al. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02143-y · Cited by 16

Unsupervised domain adaptation for histopathology image segmentation with incomplete labels

Zhou, H., Wang, Y., Zhang, B., Zhou, C., Vonsky, M. S., Mitrofanova, L. B., Zou, D., & Li, Q. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108226 · Cited by 15

A Deep Learning–Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma

Hamada, K., Murakami, R., Ueda, A., Kashima, Y., Miyagawa, C., Taki, M., Yamanoi, K., Yamaguchi, K. D., Hamanishi, J., Minamiguchi, S., et al. (2024) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.02.016 · Cited by 16

A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images

Ahmadvand, P., Farahani, H., Farnell, D., Darbandsari, A., Topham, J. T., Karasinska, J. M., Nelson, J., Naso, J. R., Jones, S. J. M., Renouf, D. J., et al. (2024) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.08.006 · Cited by 17

Dual-stage segmentation and classification framework for skin lesion analysis using deep neural network

Manzoor, K., Gilal, N. U., Agus, M., & Schneider, J. (2025) · Digital Health · doi:10.1177/20552076251351858 · Cited by 16

Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study

Gandomkar, Z., Brennan, P., & Mello-Thoms, C. R. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00181-8 · Cited by 15

Deep Learned Nucleus Features for Breast Cancer Histopathological Image Analysis based on Belief Theoretical Classifier Fusion

George, K., Faziludeen, S., Sankaran, P., & Paul, J. K. (2019) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2019.8929539 · Cited by 15

StyPath: Style-Transfer Data Augmentation for Robust Histology Image Classification

Cicalese, P. A., Mobiny, A., Yuan, P., Becker, J. U., Mohan, C., & Nguyen, H. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_34 · Cited by 15

Automated Nuclei Segmentation on Dysplastic Oral Tissues Using CNN

dos Santos, D. F. D., Tosta, T. A. A., Silva, A. B., de Faria, P. R., Travençolo, B. A. N., & do Nascimento, M. Z. (2020) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip48289.2020.9145157 · Cited by 15

A tree-based multiclassification of breast tumor histopathology images through deep learning

Murtaza, G., Wahab, A. W. A., Raza, G. M., & Shuib, L. M. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101870 · Cited by 15

Assessing Domain Adaptation Techniques for Mitosis Detection in Multi-scanner Breast Cancer Histopathology Images

Breen, J., Zucker, K., Orsi, N. M., & Ravikumar, N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97281-3_2 · Cited by 15

Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability

Zhu, J. S., Wu, W., Zhang, Y., Lin, S., Jiang, Y., Liu, R., Wang, X., & Zhang, H. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.825353 · Cited by 15

Intraoperative neuropathology of glioma recurrence: cell detection and classification

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Joint and individual analysis of breast cancer histologic images and genomic covariates

Carmichael, I. D., Calhoun, B. C., Hoadley, K. A., Troester, M. A., Geradts, J., Couture, H. D., Olsson, L. T., Perou, C. M., Niethammer, M., Hannig, J., et al. (2021) · The Annals of Applied Statistics · doi:10.1214/20-aoas1433 · Cited by 18

Deep Multi-Resolution Dictionary Learning for Histopathology Image AnalysisPreprint

Hatami, N., Bilal, M., & Rajpoot, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00669 · Cited by 15

Cross-Attention Patch Fusion for Few-Shot Colorectal Tissue Generation

Hayat, M., Dhaliwal, A. S., Din, M. M. U., Izhar, R., Nadeem, M., & Ahmad, N. (2025) · International Conference on Digital Futures and Transformative Technologies (ICoDT2) · doi:10.1109/icodt269104.2025.11360745 · Cited by 14

A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology Counting

Alahmari, S. S., Goldgof, D. B., Hall, L., & Mouton, P. R. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3213407 · Cited by 15

Computer Aided Classifier of Colorectal Cancer on Histopatological Whole Slide Images Analyzing Deep Learning Architecture Parameters

Martínez-Fernandez, E., Rojas, I., Valenzuela, O., & Rojas, I. (2023) · Applied Sciences · doi:10.3390/app13074594 · Cited by 14

Valuing vicinity: Memory attention framework for context-based semantic segmentation in histopathology

Ester, O., Hörst, F., Seibold, C., Keyl, J., Ting, S. C., Vasileiadis, N., Schmitz, J., Ivanyi, P., Grünwald, V., Bräsen, J. H., et al. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102238 · Cited by 14

Computer-Aided Breast Cancer Diagnosis Using Deep Learning: Malignancy Detection and HER2 Scoring

El-Nakeeb, M., Ali, M., AbdelHadi, K., Tealab, S. H. A., Eltohamy, M. I., & Abdel‐Hamid, L. (2023) · International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · doi:10.1109/miucc58832.2023.10278384 · Cited by 14

Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance

Niehues, J. M., Müller‐Franzes, G. A., Schirris, Y., Wagner, S. J., Jendrusch, M., Kloor, M., Pearson, A. T., Muti, H. S., Hewitt, K. J., Veldhuizen, G. P., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108410 · Cited by 17

Review of deep learning-based pathological image classification: From task-specific models to foundation models

Luan, H., Yang, K., Hu, T., Hu, J., Liu, S., Li, R., He, J., Yan, R., Guo, X., Qian, N., et al. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107578 · Cited by 15

Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images

Schupbach, J., Fasy, B. T., Sheppard, J. W., & Lawson, P. J. (2019) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2513137 · Cited by 14

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Bianconi, F., Kather, J. N., & Reyes‐Aldasoro, C. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23937-4_7 · Cited by 14

Texture Enhanced Generative Adversarial Network For Stain Normalisation In Histopathology Images

Cong, C., Liu, S., Ieva, A. D., Pagnucco, M., Berkovsky, S., & Song, Y. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433860 · Cited by 14

Cascade R-CNN for MIDOG Challenge

Razavi, S., Dambandkhameneh, F., Androutsos, D., Done, S. J., & Khademi, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97281-3_13 · Cited by 14

Deep Learning Predicts EBV Status in Gastric Cancer Based on Spatial Patterns of Lymphocyte Infiltration

Zhang, B., Yao, K., Xu, M., Wu, J., & Cheng, C. (2021) · Cancers · doi:10.3390/cancers13236002 · Cited by 14

Stain deconvolution of histology images via independent component analysis in the wavelet domain

Alsubaie, N. M., Raza, S. E. A., & Rajpoot, N. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493388 · Cited by 14

Adversarial attacks and adversarial robustness in computational pathologyPreprint

Laleh, N. G., Truhn, D., Veldhuizen, G. P., Han, T., van Treeck, M., Buelow, R. D., Langer, R., Dislich, B., Boor, P., Schulz, V., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.15.484515 · Cited by 14

Benchmarking histopathology foundation models for ovarian cancer bevacizumab treatment response prediction from whole slide images

Mallya, M., Mirabadi, A. K., Farnell, D., Farahani, H., & Bashashati, A. (2025) · Discover Oncology · doi:10.1007/s12672-025-01973-x · Cited by 20

Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness

Huang, Y., Zhao, W., Zhang, Z., Chen, Y., Fu, Y., Wu, F., Jiang, Y., Liang, L., Wang, S., & Yu, L. (2025) · Nature Communications · doi:10.1038/s41467-025-66300-y · Cited by 22

Self-Supervised Digital Histopathology Image Disentanglement for Arbitrary Domain Stain Transfer

Ling, Y., Tan, W., & Yan, B. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3298361 · Cited by 13

Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas

Ding, R., Yadav, A., Rodriguez, E. F., da Silva, A. C. A. L., & Hsu, W. W. Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107484 · Cited by 13

Enhanced Nuclei Segmentation in Histopathology Image Leveraging RGB Channels through Triple-Encoder and Single-Decoder Architectures

Sumon, R. I., Mazumdar, M. A. I., Uddin, S. M. I., Joo, M.-I., & Kim, H. (2023) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon59035.2023.10316131 · Cited by 13

Uninformed Teacher-Student for hard-samples distillation in weakly supervised mitosis localization

Fernandez‐Martín, C., Silva-Rodríguez, J., Kiraz, U., Morales, S., Janssen, E. A. M., & Naranjo, V. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102328 · Cited by 13

An ensemble of deep CNNs for automatic grading of breast cancer in digital pathology images

Sharma, S., Kumar, S., Sharma, M., & Kalkal, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-023-09368-1 · Cited by 13

Multi-headed CNN for colon cancer classification using histopathological images with tikhonov-based unsharp masking

Kumar, A., Vishwakarma, A., & Bajaj, V. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18357-y · Cited by 14

Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification

Yan, T., Chen, G., Zhang, H., Wang, G., Yan, Z., Li, Y., Xu, S., Zhou, Q., Shi, R., Tian, Z., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30889 · Cited by 13

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

Boissin, C., Wang, Y., Sharma, A., Weitz, P., Karlsson, E., Robertson, S., Hartman, J., & Rantalainen, M. (2024) · Breast Cancer Research · doi:10.1186/s13058-024-01840-7 · Cited by 13

CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images

Mali, M. T., Hançer, E., Samet, R., Yıldırım, Z., & Nemati, N. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106699 · Cited by 13

Low-Resource Finetuning of Foundation Models Beats State-of-the-Art in Histopathology

Roth, B., Koch, V., Wagner, S. J., Schnabel, J. A., Marr, C., & Peng, T. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635695 · Cited by 22

Identification of prognostic spatial organization features in colorectal cancer microenvironment using deep learning on histopathology images

Lin, Q., Ke, J., Yu, Z., Cao, Y., Lai, Y., Chen, Y., Gao, F., & Wang, X. (2021) · Medicine in Omics · doi:10.1016/j.meomic.2021.100008 · Cited by 13

Detecting Mitosis Against Domain Shift Using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge

Liang, J., Wang, C., Cheng, Y., Wang, Z., Wang, F., Huang, L., Yu, Z., & Wang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97281-3_10 · Cited by 13

Automated image analysis to assess hygienic behaviour of honeybees

Paolillo, G., Petrini, A., Casiraghi, E., Iorio, M. G. D., Biffani, S., Pagnacco, G., Minozzi, G., & Valentini, G. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263183 · Cited by 13

Large-scale extraction of interpretable features provides new insights into kidney histopathology – A proof-of-concept study

Gupta, L., Klinkhammer, B. M., Seikrit, C., Fan, N., Bouteldja, N., Gräbel, P., Gadermayr, M., Boor, P., & Merhof, D. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100097 · Cited by 13

Global and local attentional feature alignment for domain adaptive nuclei detection in histopathology images

Wang, Z., Zhu, X., Li, A., Wang, Y., Meng, G., & Wang, M. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102341 · Cited by 13

Tackling stain variability using CycleGAN-based stain augmentation

Bouteldja, N., Hölscher, D. L., Bülow, R. D., Roberts, I. S. D., Coppo, R., & Boor, P. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100140 · Cited by 14

Test-Time Image-to-Image Translation Ensembling Improves Out-of-Distribution Generalization in Histopathology

Scalbert, M., Vakalopoulou, M., & Couzinié-Devy, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_12 · Cited by 13

Colorectal Cancer Survival Prediction Using Deep Distribution Based Multiple-Instance Learning

Li, X., Jonnagaddala, J., Cen, M., Zhang, H., & Xu, S. X. (2022) · Entropy · doi:10.3390/e24111669 · Cited by 15

Automatic quantification of IHC stain in breast TMA using colour analysis

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Analysis of the Influence of Color Normalization in the Classification of Non-Hodgkin Lymphoma Images

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