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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IMPaSh: A Novel Domain-Shift Resistant Representation for Colorectal Cancer Tissue Classification

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A Quantitative Assessment of Image Normalization for Classifying Histopathological Tissue of the Kidney

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Novel method for emboli analog formation towards improved stroke retrieval devices

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Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin

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Grayscale representation of infrared microscopy images by extended multiplicative signal correction for registration with histological images

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Deep Learning Methods for Mitosis Detection in Breast Cancer Histopathological Images: A Comprehensive Review

Dif, N., & Elberrichi, Z. E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50402-1_17 · Cited by 10

A Personalized Diagnostic Generation Framework Based on Multi-source Heterogeneous Data

Wu, J., Zhang, R., Gong, T., Zhang, H., Wang, C., & Li, C. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669427 · Cited by 9

Stain separation in digital bright field histopathology

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Macrophage-mediated immunoediting drives ductal carcinoma evolution: Space is the game changerPreprint

Gatenbee, C., West, J., Baker, A. M., Guljar, N., Jones, J. L., Graham, T. A., Robertson‐Tessi, M., & Anderson, A. R. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/594598 · Cited by 9

CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology ImagesPreprint

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A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers Using Sequencer Architecture

Cen, M., Li, X., Guo, B., Jonnagaddala, J., Zhang, H., & Xu, S. X. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2023.09.006 · Cited by 11

MMV_Im2Im: an open-source microscopy machine vision toolbox for image-to-image transformation

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A Virtual Staining Method Based on Self-Supervised GAN for Fourier Ptychographic Microscopy Colorful Imaging

Wang, Y., Guan, N., Li, J., & Wang, X. (2024) · Applied Sciences · doi:10.3390/app14041662 · Cited by 9

Review of computer‐assisted diagnosis model to classify follicular lymphoma histology

Saxena, P., Aggarwal, S. K., Sinha, A., Saxena, S., & Singh, A. K. (2024) · Cell Biochemistry and Function · doi:10.1002/cbf.4088 · Cited by 8

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology

Fatemi, M., Lu, Y., Diallo, A., Srinivasan, G., Azher, Z., Christensen, B. C., Salas, L. A., Tsongalis, G. J., Palisoul, S. M., Perreard, L., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae476 · Cited by 8

Prompting Whole Slide Image Based Genetic Biomarker Prediction

Zhang, L., Yun, B., Xie, X., Li, Q., Li, X., & Wang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_38 · Cited by 11

Stress testing deep learning models for prostate cancer detection on biopsies and surgical specimens

Flannery, B. T., Sandler, H. M., Lal, P., Feldman, M. D., Santa-Rosario, J. C., Pathak, T. B., Mirtti, T. K., Farré, X., Correa, R. J. M., Chafe, S., et al. (2024) · The Journal of Pathology · doi:10.1002/path.6373 · Cited by 8

Investigating lung cancer microenvironment from cell segmentation of pathological image and its application in prognostic stratification

Zhang, X., Zhang, Z., Liu, Y., Zhao, S.-L., Zhao, X., Zhang, L., Gu, C., & Zhao, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-85532-y · Cited by 8

Prediction of colorectal cancer microsatellite instability and tumor mutational burden from histopathological images using multiple instance learning

Wang, W., Shi, W., Nie, C., Xing, W., Yang, H., Li, F., Liu, J., Tian, G., Wang, B., & Yang, J. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107608 · Cited by 10

Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms

Sumon, R. I., Mozumder, M. A. I., Akter, S., Uddin, S. M. I., Al-Onaizan, M. H. A., Alkanhel, R. I., & Muthanna, M. S. A. (2025) · Diagnostics · doi:10.3390/diagnostics15101271 · Cited by 8

A new approach combining a whole-slide foundation model and gradient boosting for predicting BRAF mutation status in dermatopathology

Albahri, M., Sauter, D., Nensa, F., Lodde, G. C., Livingstone, E., Schadendorf, D., & Kukuk, M. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.017 · Cited by 9

HCT-Net: A hybrid CNN-Transformer network for multi-class cervical cell classification

Zhao, Y., Fu, C., Xu, S., Cao, L. L., & Ma, H. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108383 · Cited by 10

Digital pathology in forensic science: a systematic review of the literature

Ambrosi, L., Amirante, F., Nicolì, S., Marzullo, A., Ingravallo, G., Ferorelli, D., Solarino, B., & Argentiero, G. (2025) · Forensic Science Medicine and Pathology · doi:10.1007/s12024-025-01146-w · Cited by 8

Geometric multi-instance learning for weakly supervised gastric cancer segmentation

Huang, C., Xia, H., Xiao, X., Hong, C. H. E. N., Jiang, Y., Lyu, Y., Ni, Z., Wang, T., Wang, N. 王., & Huang, Q. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02287-6 · Cited by 9

Evaluating stability of histomorphometric features across scanner and staining variations: predicting biochemical recurrence from prostate cancer whole slide images

Leo, P., Lee, G., & Madabhushi, A. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2217053 · Cited by 8

Recombinant Spidroin Microgel as the Base of Cell-Engineered Constructs Mediates Liver Regeneration in Rats

Shagidulin, M. Y., Онищенко, Н. А., Grechina, A., Nikolskaya, A. O., Крашенинников, М. Е., Lyundup, A. V., Volkova, E. A., Mogeiko, N. P., Venediktov, A. A., Piavchenko, G. A., et al. (2022) · Polymers · doi:10.3390/polym14153179 · Cited by 8

XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification

Abbas, A., Gaber, M. M., & Abdelsamea, M. M. (2022) · Sensors · doi:10.3390/s22249875 · Cited by 9

Truncated Normal Mixture Prior Based Deep Latent Model for Color Normalization of Histology Images

Mahapatra, S., & Maji, P. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3238425 · Cited by 8

Image Classification of Ischemic Stroke Blood Clot Origin using Stacked EfficientNet-B0, VGG19 and ResNet-152

Rao, M. V. S. R., Puligundla, S., Ekkala, N. S., & Chebrolu, S. (2023) · International Conference on Secure Cyber Computing and Communications (ICSCCC) · doi:10.1109/icsccc58608.2023.10176805 · Cited by 8

Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning

Hörst, F., Ting, S. C., Liffers, S., Pomykala, K. L., Steiger, K., Albertsmeier, M., Angele, M. K., Lorenzen, S., Quante, M., Weichert, W., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00038 · Cited by 9

Deep Learning within a DICOM WSI Viewer for Histopathology

Vállez, N., Espinosa-Aranda, J. L., Pedraza, A., Déniz, Ó., & Bueno, G. (2023) · Applied Sciences · doi:10.3390/app13179527 · Cited by 10

Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation

Qiu, J., Wilm, F., Öttl, M., Schlereth, M., Liu, C., Heimann, T., Aubreville, M., & Breininger, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43895-0_9 · Cited by 8

Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer

Chatterji, S., Niehues, J. M., van Treeck, M., Loeffler, C. M. L., Saldanha, O. L., Veldhuizen, G. P., Çifçi, D., Carrero, Z. I., Eid, R. A., Speirs, V., et al. (2023) · npj Breast Cancer · doi:10.1038/s41523-023-00599-y · Cited by 9

Histopathological Colorectal Cancer Image Classification by Using Inception V4 CNN Model

Patnaik, R. K., Rath, P. S., Padhy, S. K., & Dash, S. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-4634-1_79 · Cited by 9

A Histopathologic Image Analysis for the Classification of Endocervical Adenocarcinoma Silva Patterns Depend on Weakly Supervised Deep Learning

Liu, Q., Zhang, X., Jiang, X., Zhang, C., Li, J., Zhang, X., Yang, J., Yu, N., Zhu, Y., Liu, J., et al. (2024) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.01.016 · Cited by 8

StainSWIN: Vision transformer-based stain normalization for histopathology image analysis

Kablan, E. B., & Ayas, S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108136 · Cited by 8

Deep learning for breast cancer diagnosis from histopathological images: classification and gene expression: review

Thâalbi, O., & Akhloufi, M. A. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00489-8 · Cited by 11

Deep learning and color variability in breast cancer histopathological images: a preliminary study

Lee, G. N., Bajger, M., & Clark, K. (2018) · International Workshop on Breast Imaging (IWBI) · doi:10.1117/12.2316613 · Cited by 8

MLCD: A Unified Software Package for Cancer Diagnosis

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A Comparison Based Breast Cancer High Microscopy Image Classification Using Pre-trained Models

Ojha, N., & Kumar, A. (2020) · Students Conference on Engineering and Systems · doi:10.1109/sces50439.2020.9236708 · Cited by 8

Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging

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Progress of Machine Vision in the Detection of Cancer Cells in Histopathology

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Characterizing Continual Learning Scenarios for Tumor Classification in Histopathology Images

Kaustaban, V., Ba, Q., Bhattacharya, I., Sobh, N., Mukherjee, S., Martin, J. C., Miri, M. S., Guetter, C., & Chaturvedi, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16961-8_18 · Cited by 8

VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts

Ternes, L., Huang, G., Lanciault, C., Thibault, G., Riggers, R. R., Gray, J. W., Muschler, J. L., & Chang, Y. H. (2020) · Scientific Reports · doi:10.1038/s41598-020-78061-3 · Cited by 8

An investigation of attention mechanisms in histopathology whole-slide-image analysis for regression objectives

Weitz, P., Wang, Y., Hartman, J., & Rantalainen, M. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00074 · Cited by 8

Deep positive-unlabeled learning for region of interest localization in breast tissue images

Pati, P., Andani, S., Viana, M. P., Gabrani, M., Wild, P. J., Rüschoff, J. H., & Pediaditis, M. (2018) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2293721 · Cited by 8

Author Correction: Pan-cancer image-based detection of clinically actionable genetic alterations

Kather, J. N., Heij, L. R., Grabsch, H. I., Loeffler, C. M. L., Echle, A., Muti, H. S., Krause, J., Niehues, J., Sommer, K. A. J., Bankhead, P., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-00149-6 · Cited by 8

Deep Learning–Based Classification of Early-Stage Mycosis Fungoides and Benign Inflammatory Dermatoses on H&E-Stained Whole-Slide Images: A Retrospective, Proof-of-Concept Study

Doeleman, T., Brussee, S., Hondelink, L. M., Westerbeek, D. W. F., Sequeira, A. M., Valkema, P. A., Jansen, P. M., He, J., Vermeer, M. H., Quint, K. D., et al. (2025) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2024.07.036 · Cited by 7

A Hybridized Deep Learning Methodology for Mitosis Detection and Classification from Histopathology Images

Nemati, N., Samet, R., Hançer, E., Yıldırım, Z., & Akkas, E. E. (2023) · Journal of Machine Intelligence and Data Science · doi:10.11159/jmids.2023.005 · Cited by 7

BCD-net: Stain separation of histological images using deep variational Bayesian blind color deconvolution

Yang, S., Pérez-Bueno, F., Castro-Macías, F. M., Molina, R., & Katsaggelos, A. K. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104318 · Cited by 7

Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence

Zhou, X., Dai, J., Lu, Y., Zhao, Q., Liu, Y., Wang, C., Zhao, Z., Wang, C., Gao, Z., Yu, Y., et al. (2024) · BMC Cancer · doi:10.1186/s12885-024-13292-5 · Cited by 8

A novel dataset and a two-stage deep learning method for breast cancer mitosis nuclei identification

Wang, H., Liu, Z., Pan, X., Kang, Y., Lan, R., Guan, J., & Li, B. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104978 · Cited by 7

IBDome: An Integrated Molecular, Histopathological, and Clinical Atlas of Inflammatory Bowel Diseases

Plattner, C., Sturm, G., Kühl, A. A., Atreya, R. N., Carollo, S., Rieder, D., Gronauer, R., Günther, M., Ormanns, S., Manzl, C., et al. (2025) · Gastroenterology · doi:10.1053/j.gastro.2026.05.023 · Cited by 8

Adaptive compression framework for giga-pixel whole slide images

Lee, J., Takemaru, L., Bappy, D. M., Jeong, Y. S., Jeong, W., Oldridge, D. A., Kim, D., Ahn, S., & Lee, S. H. (2025) · Nature Communications · doi:10.1038/s41467-025-66889-0 · Cited by 10

Stain normalization gives greater generalizability than stain jittering in neural network training for the classification of coeliac disease in duodenal biopsy whole slide images

Schreiber, B. A., Denholm, J., Gilbey, J., Schönlieb, C., & Soilleux, E. J. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100324 · Cited by 7

A Novel Colorectal Histopathological Image Classification Method Based on Progressive Multi-Granularity Feature Fusion of Patch

Cao, Z., Jia, W., Jiang, H., Zhao, X., Gao, H., Si, J., & Shi, C. (2024) · IEEE Access · doi:10.1109/access.2024.3401240 · Cited by 7

Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization

Doerrich, S., Salvo, F. D., & Ledig, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_60 · Cited by 7

Cancer Metastasis Detection Through Multiple Spatial Context Network

Zhang, W., Zhu, C., Liu, J., Wang, Y., & Jin, M. (2019) · International Conference on Computing and Pattern Recognition (ICCPR) · doi:10.1145/3373509.3373567 · Cited by 7

Medical Image Super-Resolution Based on the Generative Adversarial Network

Huang, X., Zhang, Q., Wang, G., Guo, X., & Li, Z. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-32-9686-2_29 · Cited by 7

Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis

Hecht, H., Sarhan, M. H., & Popovici, V. C. (2020) · Applied Sciences · doi:10.3390/app10186427 · Cited by 8

Development of patient-specific 3D models from histopathological samples for applications in radiation therapy

DeCunha, J. M., Poole, C. M., Vallières, M., Torres, J. J., Camilleri‐Broët, S., Rayes, R. F., Spicer, J. D., & Enger, S. A. (2021) · Physica Medica · doi:10.1016/j.ejmp.2020.12.009 · Cited by 7

An adaptive digital stain separation method for deep learning-based automatic cell profile counts

Dave, P., Alahmari, S. S., Goldgof, D. B., Hall, L., Morera, H., & Mouton, P. R. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109102 · Cited by 7

Improvements in lymphocytes detection using deep learning with a preprocessing stage

Guerrero, R. E. D., & Oliveira, J. L. (2021) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms52027.2021.00068 · Cited by 7

Artificial Intelligence in Anatomic Pathology

Levy, J., & Vaickus, L. J. (2021) · Advances in Molecular Pathology · doi:10.1016/j.yamp.2021.07.005 · Cited by 6

Effect of Color Normalization on Nuclei Segmentation Problem in H&E Stained Histopathology Images

Yıldırım, Z., Hançer, E., Samet, R., Mali, M. T., & Nemati, N. (2022) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu55565.2022.9864814 · Cited by 7

Visual Deep Learning-Based Explanation for Neuritic Plaques Segmentation in Alzheimer’s Disease Using Weakly Annotated Whole Slide Histopathological Images

Jiménez, G., Kar, A., Ounissi, M., Ingrassia, L., Boluda, S., Delatour, B., Stimmer, L., & Racoceanu, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_33 · Cited by 7

Stain color translation of multi-domain OSCC histopathology images using attention gated cGAN

Barua, B., Bora, K., Kr.Das, A., Ahmed, G. N., & Rahman, T. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102202 · Cited by 7

Histo-fusion: a novel domain specific learning to identify invasive ductal carcinoma (IDC) from histopathological images

Jawad, M. A., & Khursheed, F. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15134-1 · Cited by 7

Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology

Mazaheri, P., Bidgoli, A. A., Rahnamayan, S., & Tizhoosh, H. R. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110346 · Cited by 11

Do Tissue Source Sites Leave Identifiable Signatures in Whole Slide Images Beyond Staining?

Keller, P., Dawood, M., & Minhas, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39539-0_1 · Cited by 7

DIRXNet: A Hybrid Deep Network for Classification of Breast Histopathology Images

Jothi, J. A. A., & Damania, K. (2023) · SN Computer Science · doi:10.1007/s42979-023-02413-9 · Cited by 7

A deep‐learning workflow to predict upper tract urothelial carcinoma protein‐based subtypes from H&E slides supporting the prioritization of patients for molecular testing

Angeloni, M., van Doeveren, T., Lindner, S., Volland, P., Schmelmer, J., Foersch, S., Matek, C., Stoehr, R., Geppert, C. I., Heers, H., et al. (2024) · The Journal of Pathology Clinical Research · doi:10.1002/2056-4538.12369 · Cited by 7

Deceptive learning in histopathology

Shahamatdar, S., Saeed‐Vafa, D., Linsley, D. A., Khalil, F. K., Lovinger, K. L., Li, L., McLeod, H. T., Ramachandran, S., & Serre, T. (2024) · Histopathology · doi:10.1111/his.15180 · Cited by 12

Automated classification for pathological prostate images using AdaBoost-based Ensemble Learning

Huang, C.-H., & Kalaw, E. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7849887 · Cited by 7

Vectorized persistent homology representations for characterizing glandular architecture in histology images

Chittajallu, D. R., Siekierski, N., Lee, S., Gerber, S., Beezley, J., Manthey, D., Gutman, D. A., & Cooper, L. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363562 · Cited by 7

Colorization of H&E stained tissue using Deep Learning

Samsi, S., Jones, M. P., Kepner, J., & Reuther, A. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512419 · Cited by 7

Color Transfer Method for Efficient Enhancement of Color Images and Its Application to Peripheral Blood Smear Analysis

Prasad, M. N., Prasad, K., & Navya, K. T. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9184-2_12 · Cited by 7

Learning to Evaluate Color Similarity for Histopathology Images using Triplet Networks

Choudhary, A., Wu, H., Li, T., & Wang, M. D. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342170 · Cited by 7

Deep Feature Fusion for Breast Cancer Diagnosis on Histopathology Images

Minh, H. L., Van, M. M., & Lang, T. V. (2019) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2019.8919462 · Cited by 7

Towards Population-Based Histologic Stain Normalization of Glioblastoma

Grenko, C. M., Viaene, A. N., Nasrallah, M. P., Feldman, M. D., Akbari, H., & Bakas, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46640-4_5 · Cited by 7

Unpaired Stain Style Transfer Using Invertible Neural Networks Based on Channel Attention and Long-Range Residual

Lan, J., Cai, S., Xue, Y., Gao, Q., Du, M., Zhang, H., Wu, Z., Deng, Y., Huang, Y., Tong, T., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3051188 · Cited by 7

Representative Region Based Active Learning For Histological Classification Of Colorectal Cancer

Shen, Y., & Ke, J. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433931 · Cited by 7

Automated segmentation and morphological characterization of placental intervillous space based on a single labeled image

Rabbani, A., Babaei, M., & Gharib, M. (2023) · Micron · doi:10.1016/j.micron.2023.103448 · Cited by 7

Evaluation of a predictive method for the H&E-based molecular profiling of breast cancer with deep learningPreprint

Arslan, S., Li, X., Schmidt, J., Hense, J., Geraldes, A., Bass, C., Brown, K., Marcia, A., Dewhirst, T., Pandya, P., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.04.474882 · Cited by 6

Counterfactual Diffusion Models for Interpretable Explanations of Artificial Intelligence Models in PathologyPreprint

Žigutytė, L., Lenz, T., Han, T., Hewitt, K. J., Reitsam, N. G., Foersch, S., Carrero, Z. I., Unger, M., Meneghetti, A. R., Pearson, A. T., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.29.620913 · Cited by 7

CD301 and LSECtin glycan-binding receptors of innate immune cells serve as prognostic markers and potential predictors of immune response in breast cancer subtypes

Wegscheider, A.-S., Wojahn, I., Gottheil, P., Spohn, M., Käs, J. A., Rosin, O., Ulm, B., Nollau, P., Wagener, C., Niendorf, A., et al. (2024) · Glycobiology · doi:10.1093/glycob/cwae003 · Cited by 6

Navigating Through Whole Slide Images With Hierarchy, Multi-Object, and Multi-Scale Data

Tran, M., Wagner, S., Weichert, W., Matek, C., Boxberg, M., & Peng, T. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3532728 · Cited by 7

Conformal Prediction for Uncertainty Quantification and Reliable HER2 Status Classification in Breast Cancer IHC Images

Pintawong, S., Shuangshoti, S., Jitpasutham, T., Shuangshoti, S., Wiwatwarayos, K., Kobchaisawat, T., & Chalidabhongse, T. H. (2025) · IEEE Access · doi:10.1109/access.2025.3552934 · Cited by 6

Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical features

Doyle, S., Lips, E. H., Marcus, E., Mulder, L., Liu, Y.-H., Canton, F. D., Kootstra, T., van Seijen, M., Bouybayoune, I., Sawyer, E. J., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105750 · Cited by 6

GastritisMIL: An interpretable deep learning model for the comprehensive histological assessment of chronic gastritis

Xia, K., Hu, Y., Cai, S., Lin, M., Lu, M., Lu, H., Ye, Y., Lin, F., Gao, L., Xia, Q., et al. (2025) · Patterns · doi:10.1016/j.patter.2025.101286 · Cited by 11

A Hybrid Model for Early Melanoma Detection: Integrating YOLOv9 and Faster R-CNN for Enhanced Diagnostic Accuracy

Marie, M. I., Elredeny, M. S., & Yakoub, A. (2025) · IEEE Access · doi:10.1109/access.2025.3587625 · Cited by 6

Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer

Brancato, V., Verdicchio, M., Cavaliere, C., Isgrò, F., Salvatore, M., & Aiello, M. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01841-8 · Cited by 7

VISGAB: Virtual staining-driven GAN benchmarking for optimizing skin tissue histology

Hussain, M. A., Waris, A., Akram, M. U., Khan, M. J., Asaf, M. Z., Javaid, A., Sahrish, F., Gilani, S. O., & Hazzazi, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-26493-0 · Cited by 7

Staining normalization in histopathology: Method benchmarking using multicenter dataset

Khan, U., Härkönen, J., Friman, M., Hakimnejad, H., Latonen, L., Kuopio, T., & Ruusuvuori, P. (2026) · Scientific Reports · doi:10.1038/s41598-026-40943-3 · Cited by 15

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Pour, E. S., Esmaeili, M., & Romoozi, M. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i6.pp6397-6409 · Cited by 6

Regional realness-aware generative adversarial networks for stain normalization

Kablan, E. B. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08659-x · Cited by 6

A Mitosis Detection and Classification Methodology with YOLOv5 and Fuzzy Classifiers

Nemati, N., Samet, R., Hançer, E., Yıldırım, Z., & Mali, M. T. (2023) · Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science · doi:10.11159/mvml23.111 · Cited by 6

CytoGAN: Unpaired staining transfer by structure preservation for cytopathology image analysis

Wang, R., Yang, S., Li, Q., & Zhong, D. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108942 · Cited by 6

Stain SAN: simultaneous augmentation and normalization for histopathology images

Kim, T., Li, Y., Calhoun, B. C., Thennavan, A., Carey, L., Symmans, W. F., Troester, M. A., Perou, C. M., & Marron, J. S. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.4.044006 · Cited by 6

Prediction of gene expression-based breast cancer proliferation scores from histopathology whole slide images using deep learning

Ekholm, A., Wang, Y., Vallon‐Christersson, J., Boissin, C., & Rantalainen, M. (2024) · BMC Cancer · doi:10.1186/s12885-024-13248-9 · Cited by 8

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