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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Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images

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Wide residual networks for mitosis detection

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Automated Detection and Grading of Non–Muscle-Invasive Urothelial Cell Carcinoma of the Bladder

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Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

Wong, A. N. N., He, Z., Leung, K. L., To, C. C. K., Wong, C. Y., Wong, S. C. C., Yoo, J. S., Chan, C. K. R., Chan, A. Z., Lacambra, M. D., et al. (2022) · Cancers · doi:10.3390/cancers14153780 · Cited by 64

Colour adaptive generative networks for stain normalisation of histopathology images

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A deep learning approach for mitosis detection: Application in tumor proliferation prediction from whole slide images

Nateghi, R., Danyali, H., & Helfroush, M. S. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102048 · Cited by 57

Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images

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A novel method for tissue segmentation in high-resolution H&E-stained histopathological whole-slide images

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Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

Abbet, C., Zlobec, I., Bozorgtabar, B., & Thiran, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_46 · Cited by 55

Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study

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Harnessing artificial intelligence for prostate cancer management

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Structure-Preserving Multi-domain Stain Color Augmentation Using Style-Transfer with Disentangled Representations

Wagner, S. J., Khalili, N., Sharma, R., Boxberg, M., Marr, C., de Back, W., & Peng, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_25 · Cited by 52

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Brixtel, R., Bougleux, S., Lézoray, O., Caillot, Y., Lemoine, B., Fontaine, M., Nebati, D., & Renouf, A. (2022) · IEEE Access · doi:10.1109/access.2022.3227437 · Cited by 63

A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application

Mokoatle, M., Marivate, V. N., Mapiye, D. S., Bornman, R. M. S., & Hayes, V. M. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05235-x · Cited by 63

Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance

Mahapatra, D., Bozorgtabar, B., Thiran, J., & Shao, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_30 · Cited by 52

A systematic pan-cancer study on deep learning-based prediction of multi-omic biomarkers from routine pathology images

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Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma

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Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer

Xu, H., Park, S., Clemenceau, J. R. V., Choi, J., Radakovich, N., Sh, L., & Hwang, T. H. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100105 · Cited by 50

Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model

Khan, A., Brouwer, N. P. M., Blank, A., Müller, F. C., Soldini, D., Noske, A., Gaus, E., Brandt, S., Nagtegaal, I. D., Dawson, H., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100118 · Cited by 49

Automatic data augmentation to improve generalization of deep learning in H&E stained histopathology

Faryna, K., van der Laak, J. A. W. M., & Litjens, G. J. S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108018 · Cited by 55

A Federated Learning System for Histopathology Image Analysis With an Orchestral Stain-Normalization GAN

Shen, Y., Sowmya, A., Luo, Y., Liang, X., Shen, D. G., & Ke, J. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3221724 · Cited by 58

Learning Domain-Invariant Representations of Histological Images

Lafarge, M. W., Pluim, J. P. W., Eppenhof, K. A. J., & Veta, M. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00162 · Cited by 54

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Chen, S., Ding, C., & Tao, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_27 · Cited by 49

Towards histopathological stain invariance by Unsupervised Domain Augmentation using generative adversarial networks

Vasiljević, J., Feuerhake, F., Wemmert, C., & Lampert, T. A. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.005 · Cited by 48

A proinflammatory stem cell niche drives myelofibrosis through a targetable galectin-1 axis

Li, R., Colombo, M., Wang, G., Rodriguez-Romera, A., Benlabiod, C., Jooss, N. J., O’Sullivan, J., Brierley, C. K., Clark, S., Pérez‐Sáez, J. M., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adj7552 · Cited by 53

Segmentation methods of H&E-stained histological images of lymphoma: A review

Tosta, T. A. A., Neves, L. A., & do Nascimento, M. Z. (2017) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.05.009 · Cited by 48

Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping

Javed, S., Mahmood, A., Werghi, N., Benes, K., & Rajpoot, N. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3023795 · Cited by 48

A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia

Shephard, A., Bashir, R. M. S., Mahmood, H., Jahanifar, M., Minhas, F., Raza, S. E. A., McCombe, K. D., Craig, S. G., James, J. A., Brooks, J. M., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00624-8 · Cited by 56

Quantitative analysis of stain variability in histology slides and an algorithm for standardization

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Localized Energy-Based Normalization of Medical Images: Application to Chest Radiography

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An approach for detection of glomeruli in multisite digital pathology

Marée, R., Dallongeville, S., Olivo‐Marín, J., & Meas‐Yedid, V. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493442 · Cited by 47

CoMHisP: A Novel Feature Extractor for Histopathological Image Classification Based on Fuzzy SVM With Within-Class Relative Density

Kumar, A., Singh, S. K., Saxena, S., Singh, A. K., Shrivastava, S., Lakshmanan, K. S., Kumar, N., & Singh, R. K. (2020) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.2995968 · Cited by 46

GLNET: global–local CNN's-based informed model for detection of breast cancer categories from histopathological slides

Khan, S. U. R., Zhao, M., Asif, S., Chen, X., & Zhu, Y. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05742-x · Cited by 46

A comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification

Breen, J., Allen, K., Zucker, K., Godson, L., Orsi, N. M., & Ravikumar, N. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00799-8 · Cited by 61

Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer

Niazi, M. K. K., Yao, K., Zynger, D. L., Clinton, S. K., Chen, J., Koyutürk, M., LaFramboise, T., & Gürcan, M. N. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2565515 · Cited by 46

GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows

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A Multi-Stage Approach to Breast Cancer Classification Using Histopathology Images

Bagchi, A., Pramanik, P., & Sarkar, R. (2022) · Diagnostics · doi:10.3390/diagnostics13010126 · Cited by 45

Data-driven color augmentation for H&E stained images in computational pathology

Marini, N., Otálora, S., Wodziński, M., Tomassini, S., Dragoni, A. F., Marchand‐Maillet, S., Dominguez-Morales, J. P., Durán-López, L., Vatrano, S., Müller, H., et al. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100183 · Cited by 49

Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks with Hierarchical Loss and Global Pooling

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Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology

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Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning

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Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

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Deep learning approaches for breast cancer detection in histopathology images: A review

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Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge

Aubreville, M., Stathonikos, N., Donovan, T., Klopfleisch, R., Ammeling, J., Ganz, J., Wilm, F., Veta, M., Jabari, S., Eckstein, M., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103155 · Cited by 53

Algorithm and benchmark dataset for stain separation in histology images

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An innovative neural network framework to classify blood vessels and tubules based on Haralick features evaluated in histological images of kidney biopsy

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Tumor Cell Load and Heterogeneity Estimation From Diffusion-Weighted MRI Calibrated With Histological Data: an Example From Lung Cancer

Yin, Y., Sedlaczek, O., Müller, B., Warth, A., González‐Vallinas, M., Lahrmann, B., Grabe, N., Kauczor, H., Breuhahn, K., Vignon-Clementel, I. E., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2698525 · Cited by 44

Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification

Baroni, G. L., Rasotto, L., Roitero, K., Tulisso, A., Loreto, C. D., & Mea, V. D. (2024) · Journal of Imaging · doi:10.3390/jimaging10050108 · Cited by 43

Spatial gene expression at single-cell resolution from histology using deep learning with GHIST

Fu, X., Cao, Y., Bian, B., Wang, C., Graham, J. D., Pathmanathan, N., Patrick, E., Kim, J., & Yang, J. Y. H. (2025) · Nature Methods · doi:10.1038/s41592-025-02795-z · Cited by 51

Developing image analysis pipelines of whole-slide images: Pre- and post-processing

Smith, B. H., Hermsen, M., Lesser, E. R., Ravichandar, D., & Kremers, W. K. (2020) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.531 · Cited by 43

Style transfer strategy for developing a generalizable deep learning application in digital pathology

Shin, S. J., You, S. C., Jeon, H., Jung, J. W., An, M. H., Park, R. W., & Roh, J. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105815 · Cited by 42

A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images

Oyelade, O. N., & Ezugwu, A. E.-S. (2021) · Scientific Reports · doi:10.1038/s41598-021-98978-7 · Cited by 43

Stable and discriminating features are predictive of cancer presence and Gleason grade in radical prostatectomy specimens: a multi-site study

Leo, P., Elliott, R. M., Shih, N. N. C., Gupta, S., Feldman, M. D., & Madabhushi, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-33026-5 · Cited by 41

Computer assisted recognition of breast cancer in biopsy images via fusion of nucleus-guided deep convolutional features

George, K., Sankaran, P., & Paul Joseph, K. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105531 · Cited by 43

GLCM and GLRLM Based Texture Analysis: Application to Brain Cancer Diagnosis Using Histopathology Images

Durgamahanthi, V., Christaline, J. A., & Edward, A. S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5566-4_61 · Cited by 42

Multifaceted fused-CNN based scoring of breast cancer whole-slide histopathology images

Wahab, N., & Khan, A. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106808 · Cited by 42

A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Liew, X. Y., Hameed, N., & Clos, J. (2021) · Cancers · doi:10.3390/cancers13112764 · Cited by 46

AI in Computational Pathology of Cancer: Improving Diagnostic Workflows and Clinical Outcomes?

Çifçi, D., Veldhuizen, G. P., Foersch, S., & Kather, J. N. (2023) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-061521-092038 · Cited by 41

Noninvasive virtual biopsy using micro-registered optical coherence tomography (OCT) in human subjects

Winetraub, Y., Vleck, A. V., Yuan, E. C., Terem, I., Zhao, J., Yu, C., Chan, W., Do, H., Shevidi, S., Mao, M., et al. (2024) · Science Advances · doi:10.1126/sciadv.adi5794 · Cited by 41

A novel convolution transformer-based network for histopathology-image classification using adaptive convolution and dynamic attention

Mahmood, T., Wahid, A., Hong, J. S., Kim, S. G., & Park, K. R. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108824 · Cited by 43

Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion

Mahbod, A., Ellinger, I., Ecker, R., Smedby, Ö., & Wang, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_85 · Cited by 41

An ensemble deep learning for automatic prediction of papillary thyroid carcinoma using fine needle aspiration cytology

Duc, N. T., Lee, Y., Park, J. H., & Lee, B. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115927 · Cited by 41

Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection

Abbet, C., Studer, L., Fischer, A., Dawson, H., Zlobec, I., Bozorgtabar, B., & Thiran, J. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102473 · Cited by 42

R/DWD: distance-weighted discrimination for classification, visualization and batch adjustment

Huang, H., Lu, X., Liu, Y., Haaland, P., & Marron, J. S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts096 · Cited by 41

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer

Rączkowska, A., Paśnik, I., Kukiełka, M., Nicoś, M., Budzinska, M. A., Kucharczyk, T., Szumiło, J., Krawczyk, P. A., Crosetto, N., & Szczurek, E. (2022) · BMC Cancer · doi:10.1186/s12885-022-10081-w · Cited by 45

Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer

Zhou, Y., Koyuncu, C., Lu, C. Y., Grobholz, R., Katz, I. A., Madabhushi, A., & Janowczyk, A. R. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102702 · Cited by 40

PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification

Yao, K., Huang, K., Sun, J., & Hussain, A. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3281864 · Cited by 43

An alternative reference space for H&E color normalization

Zarella, M. D., Yeoh, C., Breen, D. E., & Garcia, F. U. (2017) · PLoS ONE · doi:10.1371/journal.pone.0174489 · Cited by 40

A Medically Assisted Model for Precise Segmentation of Osteosarcoma Nuclei on Pathological Images

Wu, J., Yuan, T., Zeng, J., & Gou, F. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3278303 · Cited by 39

Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma

Vollmer, A., Hartmann, S., Vollmer, M., Shavlokhova, V., Brands, R. C., Kübler, A. C., Wollborn, J., Hassel, F., Couillard‐Després, S., Lang, G. M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-56172-5 · Cited by 39

Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images

Hetz, M. J., Bucher, T.-C., & Brinker, T. J. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103149 · Cited by 42

PST-Diff: Achieving High-Consistency Stain Transfer by Diffusion Models With Pathological and Structural Constraints

He, Y., Liu, Z., Qi, M., Ding, S., Zhang, P., Song, F., Ma, C., Wu, H., Cai, R., Feng, Y., et al. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3430825 · Cited by 42

Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images

Tafavvoghi, M., Sildnes, A., Rakaee, M., Shvetsov, N., Bongo, L. A., Busund, L., & Møllersen, K. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100410 · Cited by 47

Novel Color Normalization Method for Hematoxylin & Eosin Stained Histopathology Images

Roy, S., Lal, S., & Kini, J. R. (2019) · IEEE Access · doi:10.1109/access.2019.2894791 · Cited by 39

Fast GPU-Enabled Color Normalization for Digital Pathology

Anand, D., Ramakrishnan, G., & Sethi, A. (2019) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2019.8787328 · Cited by 39

A novel approach combined transfer learning and deep learning to predict TMB from histology image

Wang, L., Jiao, Y., Qiao, Y., Zeng, N., & Yu, R. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.04.008 · Cited by 40

ImmunoAIzer: A Deep Learning-Based Computational Framework to Characterize Cell Distribution and Gene Mutation in Tumor Microenvironment

Bian, C., Wang, Y., Lü, Z., An, Y., Wang, H., Kong, L., Du, Y., & Tian, J. (2021) · Cancers · doi:10.3390/cancers13071659 · Cited by 43

Multi-scale relational graph convolutional network for multiple instance learning in histopathology images

Bazargani, R., Fazli, L., Gleave, M. E., Goldenberg, L., Bashashati, A., & Salcudean, S. E. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103197 · Cited by 42

A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation

He, B., Wang, L., Zhou, W., Liu, H., Wang, Y., Lv, K., & He, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-91420-2 · Cited by 42

A Model based Survey of Colour Deconvolution in Diagnostic Brightfield Microscopy: Error Estimation and Spectral Consideration

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Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks

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