A method for normalizing histology slides for quantitative analysis

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Circular Mixture Modeling of Color Distribution for Blind Stain Separation in Pathology Images

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Empiric Methods to Account for Pre-analytical Variability in Digital Histopathology in Frontotemporal Lobar Degeneration

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MITOS-RCNN: A Novel Approach to Mitotic Figure Detection in Breast Cancer Histopathology Images using Region Based Convolutional Neural NetworksPreprint

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ProGleason-GAN: Conditional progressive growing GAN for prostatic cancer Gleason grade patch synthesis

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Unsupervised Multi-Domain Progressive Stain Transfer Guided by Style Encoding Dictionary

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Histopathology-based protein multiplex generation using deep learning

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Microbiome-transcriptome-histology triad enhances survival risk stratification in multiple cancers

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A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections

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Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

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xDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System in Colorectal Cancer

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Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images

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A Heteromorphous Deep CNN Framework for Medical Image Segmentation Using Local Binary Pattern

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State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs)

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Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies

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A TV-based image processing framework for blind color deconvolution and classification of histological images

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Registered multi-device/staining histology image dataset for domain-agnostic machine learning models

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Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization

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A generalised vision transformer-based self-supervised model for diagnosing and grading prostate cancer using histological images

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Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning

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Class-Agnostic Weighted Normalization of Staining in Histopathology Images Using a Spatially Constrained Mixture Model

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Learning to segment images with classification labels

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Mitotic Nuclei Detection in Breast Histopathology Images using YOLOv4

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Stain transfer using Generative Adversarial Networks and disentangled features

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Impact of scanner variability on lymph node segmentation in computational pathology

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A framework for falsifiable explanations of machine learning models with an application in computational pathology

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Stain-Independent Deep Learning–Based Analysis of Digital Kidney Histopathology

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Multiple-instance-learning-based detection of coeliac disease in histological whole-slide images

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Bias reduction in representation of histopathology images using deep feature selection

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GraphLSurv: A scalable survival prediction network with adaptive and sparse structure learning for histopathological whole-slide images

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An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images

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Hierarchical Discriminative Learning Improves Visual Representations of Biomedical Microscopy

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Histopathology image classification: highlighting the gap between manual analysis and AI automation

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Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer

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Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning

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Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance

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AMIAC: adaptive medical image analyzes and classification, a robust self-learning framework

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PathOrchestra: a comprehensive foundation model for computational pathology with over 100 diverse clinical-grade tasks

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Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects

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Multi-channel auto-encoders for learning domain invariant representations enabling superior classification of histopathology images

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Comparison of deep learning models for digital H&E staining from unpaired label-free multispectral microscopy images

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A unified method to revoke the private data of patients in intelligent healthcare with audit to forget

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Digitally adjusting chromogenic dye proportions in brightfield microscopy images

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A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images

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Optimized detection and segmentation of nuclei in gastric cancer images using stain normalization and blurred artifact removal

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Spatiotemporal Profiling Defines Persistence and Resistance Dynamics during Targeted Treatment of Melanoma

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Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence–Assisted Cancer Diagnosis

Ji, X., Salmon, R. M., Mulliqi, N., Khan, U., Wang, Y., Blilie, A., Olsson, H., Pedersen, B. G., Sørensen, K. D., Ulhøi, B. P., et al. (2025) · Modern Pathology · doi:10.1016/j.modpat.2025.100715 · Cited by 30

Machine Learning Achieves Pathologist-Level Celiac Disease Diagnosis

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HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer

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Fundamentals of Texture Processing for Biomedical Image Analysis

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Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions

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StainDiff: Transfer Stain Styles of Histology Images with Denoising Diffusion Probabilistic Models and Self-ensemble

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Ink Removal from Histopathology Whole Slide Images by Combining Classification, Detection and Image Generation Models

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Conceptual data sampling for breast cancer histology image classification

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Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides

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Multi-classification of breast cancer pathology images based on a two-stage hybrid network

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Virtual staining for pathology: Challenges, limitations and perspectives

Lin, W., Hu, Y., Zhu, R., Wang, B., & Wang, L. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.005 · Cited by 24

Evaluation of Kidney Histological Images Using Unsupervised Deep Learning

Sato, N., Uchino, E., Kojima, R., Sakuragi, M., Hiragi, S., Minamiguchi, S., Haga, H., Yokoi, H., Yanagita, M., & Okuno, Y. (2021) · Kidney International Reports · doi:10.1016/j.ekir.2021.06.008 · Cited by 20

Automating Ground Truth Annotations for Gland Segmentation Through Immunohistochemistry

Kataria, T., Rajamani, S., Ayubi, A. B., Bronner, M. P., Jedrzkiewicz, J., Knudsen, B. S., & Elhabian, S. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100331 · Cited by 21

Automatic measurement of melanoma depth of invasion in skin histopathological images

Xu, H., Berendt, R. C., Jha, N., & Mandal, M. K. (2017) · Micron · doi:10.1016/j.micron.2017.03.004 · Cited by 20

Weakly-Supervised Deep Stain Decomposition for Multiplex IHC Images

Abousamra, S., Fassler, D., Hou, L., Zhang, Y., Gupta, R., Kurç, T., Escobar‐Hoyos, L. F., Samaras, D., Knudson, B., Shroyer, K. R., et al. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098652 · Cited by 20

Automated gleason grading on prostate biopsy slides by statistical representations of homology profile

Yan, C., Nakane, K., Wang, X., Fu, Y. S., Lu, H., Fan, X., Feldman, M. D., Madabhushi, A., & Xu, J. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105528 · Cited by 20

Fast, Self Supervised, Fully Convolutional Color Normalization Of H&E Stained Images

Patil, A., Talha, M., Bhatia, A., Kurian, N. C., Mangale, S., Patel, S., & Sethi, A. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434121 · Cited by 20

Impact of Tissue Thickness on Computational Quantification of Features in Whole Slide Images for Diagnostic Pathology

Shah, M. P., Polónia, A., Curado, M., Vale, J., Janowczyk, A. R., & Eloy, C. (2025) · Endocrine Pathology · doi:10.1007/s12022-025-09855-2 · Cited by 19

A lightweight method for precise small lesion detection in diabetic retinopathy

Han, Z., Ye, Z., Liang, R., Dai, W., Xiao, H., Wang, X., Wu, W., Hong, C., Zheng, Q., Deng, S., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108006 · Cited by 25

Image and statistical analysis of melanocytic histology

Miedema, J., Marron, J. S., Niethammer, M., Borland, D., Woosley, J. T., Coposky, J., Wei, S. H., Reisner, H. M., & Thomas, N. E. (2012) · Histopathology · doi:10.1111/j.1365-2559.2012.04229.x · Cited by 19

Feature-Based Representation Improves Color Decomposition and Nuclear Detection Using a Convolutional Neural Network

Khoshdeli, M., & Parvin, B. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2711529 · Cited by 20

Deep Learning for Analysis of Collagen Fiber Organization in Scar Tissue

Pham, T. T. A., Kim, H., Lee, Y., Kang, H. W., & Park, S. (2021) · IEEE Access · doi:10.1109/access.2021.3097370 · Cited by 19

The Possibility of Combining and Implementing Deep Neural Network Compression Methods

Predić, B., Vukić, U., Saracevic, M. H., Karabašević, D., & Stanujkić, D. (2022) · Axioms · doi:10.3390/axioms11050229 · Cited by 19

Deep and dense convolutional neural network for multi category classification of magnification specific and magnification independent breast cancer histopathological images

Jawad, M. A., & Khursheed, F. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103935 · Cited by 19

Identify Representative Samples by Conditional Random Field of Cancer Histology Images

Shen, Y., Shen, D. G., & Ke, J. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3198526 · Cited by 19

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Pedersen, A., Smistad, E., Rise, T. V., Dale, V. G., Pettersen, H. S., Nordmo, T.-A. S., Bouget, D., Reinertsen, I., & Valla, M. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.971873 · Cited by 21

Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study

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Bias reduction using combined stain normalization and augmentation for AI-based classification of histological images

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FF-CNN: An Efficient Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images

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Variational Bayesian Blind Color Deconvolution of Histopathological Images

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Using Deep Learning in Ultrasound Imaging of Bicipital Peritendinous Effusion to Grade Inflammation Severity

Lin, B.-S., Chen, J., Tu, Y.-H., Shih, Y.-X., Lin, Y.-C., Chi, W.-L., & Wu, Y. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2968815 · Cited by 18

Temperature dependence and histological correlation of inhomogeneous magnetization transfer and myelin water imaging in ex vivo brain

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Developing ovine mammary terminal duct lobular units have a dynamic mucosal and stromal immune microenvironment

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Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning

Önder, D., Sarıoğlu, S., & Karaçalı, B. (2013) · Micron · doi:10.1016/j.micron.2013.01.003 · Cited by 19

Clinically-inspired automatic classification of ovarian carcinoma subtypes

BenTaieb, A., Nosrati, M. S., Li-Chang, H. H., Huntsman, D. D., & Hamarneh, G. (2016) · Journal of Pathology Informatics · doi:10.4103/2153-3539.186899 · Cited by 22

Uncertainty-aware ensemble of foundation models differentiates glioblastoma from its mimics

Zhao, J., Lin, S.-Y., Attias, R., Mathews, L., Engel, C., Larghero, G., Vremenko, D., Kao, T., Lee, T.-H., Wang, Y.-H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64249-6 · Cited by 21

Scalable mapping of myelin and neuron density in the human brain with micrometer resolution

Chang, S., Varadarajan, D., Yang, J., Chen, A., Kura, S., Magnain, C. V., Augustinack, J. C., Fischl, B. R., Greve, D. N., Boas, D. A., et al. (2022) · Scientific Reports · doi:10.1038/s41598-021-04093-y · Cited by 18

A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

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StainCUT: Stain Normalization with Contrastive Learning

Pérez, J. C. G., Baguer, D. O., & Maaß, P. (2022) · Journal of Imaging · doi:10.3390/jimaging8070202 · Cited by 18

Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images

Oyelade, O. N., Ezugwu, A. E.-S., Venter, H. S., Mirjalili, S., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105943 · Cited by 18

Style Transfer with Bio-realistic Appearance Manipulation for Skin-tone Inclusive rPPG

Ba, Y., Wang, Z., Karinca, D., Bozkurt, O. D., & Kadambi, A. (2022) · IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccp54855.2022.9887649 · Cited by 19

Shortcomings and areas for improvement in digital pathology image segmentation challenges

Foucart, A., Debeir, O., & Decaestecker, C. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102155 · Cited by 21

PathNarratives: Data annotation for pathological human-AI collaborative diagnosis

Zhang, H., He, Y., Wu, X., Huang, P., Qin, W., Wang, F., Ye, J., Huang, X., Liao, Y., Chen, H., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2022.1070072 · Cited by 19

Decentralized Federated Learning and Deep Learning Leveraging XAI-Based Approach to Classify Colorectal Cancer

Arthi, N. T., Mubin, K. E., Rahman, J. R., Rafi, G. M., Sheja, T. T., Reza, M. T., & Alam, M. A. (2022) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde56538.2022.10089344 · Cited by 18

A stain color normalization with robust dictionary learning for breast cancer histological images processing

Tosta, T. A. A., Freitas, A. D., de Faria, P. R., Neves, L. A., Martins, A. S., & do Nascimento, M. Z. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104978 · Cited by 18

MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception Loss

Nazki, H., Arandjelović, O., Um, I. H., & Harrison, D. J. (2023) · ACM Symposium on Applied Computing · doi:10.1145/3555776.3578612 · Cited by 18

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images

Li, Y., Chou, H., Lin, P., Shen, M., & Hsieh, S. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04530-8 · Cited by 18

Semi-supervised Adversarial Learning for Stain Normalisation in Histopathology Images

Cong, C., Liu, S., Ieva, A. D., Pagnucco, M., Berkovsky, S., & Song, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_56 · Cited by 18

Multimodal deep learning: An improvement in prognostication or a reflection of batch effect?

Howard, F. M., Kather, J. N., & Pearson, A. T. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.025 · Cited by 18

Communicator-Driven Data Preprocessing Improves Deep Transfer Learning of Histopathological Prediction of Pancreatic Ductal Adenocarcinoma

Kronberg, R. M., Häberle, L., Pfaus, M., Xu, H. C., Krings, K. S., Schlensog, M., Rau, T. T., Pandyra, A. A., Lang, K. S., Esposito, I., et al. (2022) · Cancers · doi:10.3390/cancers14081964 · Cited by 18

Quantifying the effect of color processing on blood and damaged tissue detection in Whole Slide Images

Kanwal, N., Fuster, S., Khoraminia, F., Zuiverloon, T. C. M., Rong, C., & Engan, K. (2022) · Multidimensional Signal Processing Workshop · doi:10.1109/ivmsp54334.2022.9816283 · Cited by 17

Senescence‐based colorectal cancer subtyping reveals distinct molecular characteristics and therapeutic strategies

Lv, M., Cai, D., Li, C., Chen, J., Li, G., Hu, C., Gai, B., Lei, J., Lan, P., Wu, X., et al. (2023) · MedComm · doi:10.1002/mco2.333 · Cited by 17

Identification of lymph node metastasis in pre‐operation cervical cancer patients by weakly supervised deep learning from histopathological whole‐slide biopsy images

Liu, Q., Jiang, N., Hao, Y., Hao, C., Wang, W., Bian, T., Wang, X., Li, H., Zhang, Y., Kang, Y., et al. (2023) · Cancer Medicine · doi:10.1002/cam4.6437 · Cited by 17

Automated detection of microscopic placental features indicative of maternal vascular malperfusion using machine learning

Patnaik, P., Khodaee, A., Vasam, G., Mukherjee, A., Salsabili, S., Ukwatta, E., Grynspan, D., Chan, A. D. C., & Bainbridge, S. A. (2023) · Placenta · doi:10.1016/j.placenta.2023.11.005 · Cited by 17

Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction

Elforaici, M. E. A., Montagnon, E., Romero, F., Le, W., Azzi, F., Trudel, D., Nguyen, B., Turcotte, S., Tang, A., & Kadoury, S. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103346 · Cited by 19

Appearance Normalization of Histology Slides

Niethammer, M., Borland, D., Marron, J. S., Woosley, J. T., & Thomas, N. E. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15948-0_8 · Cited by 67

Stain normalization methods for histopathology image analysis: A comprehensive review and experimental comparison

Hoque, M. Z., Keskinarkaus, A., Nyberg, P., & Seppänen, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101997 · Cited by 153

Image normalization for quantitative immunohistochemistry in digital pathology

Eycke, Y. V., Allard, J., Derock, M., Salmon, I. J., Debeir, O., & Decaestecker, C. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493386 · Cited by 2

Structure-Preserving Stain Transfer for Histopathology Images: A Technical NotePreprint

Akshat, G. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22092811

Stain Removal Through Color Normalization of Haematoxylin and Eosin Images: A Review

Lakshmanan, B., Anand, S., & Jenitha, T. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1362/1/012108 · Cited by 29