Machine learning methods for histopathological image analysis: updates in 2024

A Threshold Selection Method from Gray-Level Histograms

Otsu, N. (1979) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,748 · see citing works

Accurate structure prediction of biomolecular interactions with AlphaFold 3

Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · see citing works

Learning Transferable Visual Models From Natural Language Supervision

Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · see citing works

The 2021 WHO classification of tumors of the central nervous system: a summary

Louis, D. N., Perry, A., Wesseling, P., Brat, D. J., Cree, I. A., Figarella-Branger, D., Hawkins, C., Ng, H. K., Pfister, S. M., Reifenberger, G., et al. (2021) · Neuro-Oncology · doi:10.1093/neuonc/noab106 · Cited by 12,273 · see citing works

The cancer genome atlas pan-cancer analysis project

The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. (2013) · Nature Genetics · doi:10.1038/ng.2764 · Cited by 9,092 · see citing works

QuPath: Open source software for digital pathology image analysis

Bankhead, P., Loughrey, M. B., Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., McQuaid, S., Gray, R. T., Murray, L. J., Coleman, H. G., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-17204-5 · Cited by 8,908 · see citing works

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01228-7 · Cited by 6,909 · see citing works

Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks

Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00097 · Cited by 3,639 · see citing works

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works

Data-efficient and weakly supervised computational pathology on whole-slide images

Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282 · see citing works

Accurate proteome-wide missense variant effect prediction with AlphaMissense

Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science · doi:10.1126/science.adg7492 · Cited by 2,163 · see citing works

CoCa: Contrastive Captioners are Image-Text Foundation ModelsPreprint

Yu, J.-H., Wang, Z., Vasudevan, V., Yeung, L., Seyedhosseini, M., & Wu, Y.-H. (2022) · Trans. Mach. Learn. Res. · doi:10.48550/arxiv.2205.01917 · Cited by 1,899 · see citing works

Towards a general-purpose foundation model for computational pathology

Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02857-3 · Cited by 1,560 · see citing works

A method for normalizing histology slides for quantitative analysis

Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., Schmitt, C., & Thomas, N. E. (2009) · 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro · doi:10.1109/isbi.2009.5193250 · Cited by 1,482 · see citing works

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458 · see citing works

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Graham, S., Vu, Q. D., Raza, S. E. A., Azam, A. S., Tsang, Y. W., Kwak, J. T., & Rajpoot, N. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101563 · Cited by 1,445 · see citing works

iBOT: Image BERT Pre-Training with Online TokenizerPreprint

Zhou, J., Wei, C., Wang, H., Shen, W., Xie, C., Yuille, A., & Kong, T. (2021) · arXiv.org · doi:10.48550/arxiv.2111.07832 · Cited by 1,275 · see citing works

Predicting cancer outcomes from histology and genomics using convolutional networks

Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084 · see citing works

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., et al. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002730 · Cited by 1,082 · see citing works

DINOv2: Learning Robust Visual Features without Supervision

Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al. (2023) · arXiv · doi:10.48550/arxiv.2304.07193 · Cited by 1,078 · see citing works

Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. (2016) · Nature Communications · doi:10.1038/ncomms12474 · Cited by 972 · see citing works

A visual-language foundation model for computational pathology

Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L. P., Gerber, G., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02856-4 · Cited by 966 · see citing works

Machine Learning Methods for Histopathological Image Analysis

Komura, D., & Ishikawa, S. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.001 · Cited by 937 · see citing works

A whole-slide foundation model for digital pathology from real-world data

Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07441-w · Cited by 921 · see citing works

Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images

Vahadane, A., Peng, T., Sethi, A., Albarqouni, S., Wang, L., Baust, M., Steiger, K., Schlitter, A. M., Esposito, I., & Navab, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2529665 · Cited by 833 · see citing works

Transformer-based unsupervised contrastive learning for histopathological image classification

Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102559 · Cited by 751 · see citing works

Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis

Chen, R. J., Lu, M. Y., Wang, J., Williamson, D. F. K., Rodig, S. J., Lindeman, N. I., & Mahmood, F. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3021387 · Cited by 729 · see citing works

A visual–language foundation model for pathology image analysis using medical twitter

Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J., & Zou, J. (2023) · Nature Medicine · doi:10.1038/s41591-023-02504-3 · Cited by 693 · see citing works

Deep learning in cancer pathology: a new generation of clinical biomarkers

Echle, A., Rindtorff, N. T., Brinker, T. J., Luedde, T., Pearson, A. T., & Kather, J. N. (2021) · British Journal of Cancer · doi:10.1038/s41416-020-01122-x · Cited by 667 · see citing works

Pan-cancer integrative histology-genomic analysis via multimodal deep learning

Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Noor, Z., Shaban, M., Shady, M., Williams, M., Joo, B., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.004 · Cited by 660 · see citing works

Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning

Rivenson, Y., Wang, H., Wei, Z., Haan, K. D., Zhang, Y., Wu, Y., Günaydın, H., Zuckerman, J. E., Chong, T., Sisk, A. E. J., et al. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0362-y · Cited by 653 · see citing works

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.-M., Ciompi, F., & van der Laak, J. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101544 · Cited by 649 · see citing works

AI-based pathology predicts origins for cancers of unknown primary

Lu, M. Y., Chen, T. Y., Williamson, D. F. K., Zhao, M., Shady, M., Lipkova, J., & Mahmood, F. (2021) · Nature · doi:10.1038/s41586-021-03512-4 · Cited by 636 · see citing works

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

Chen, R. J., Chen, C., Li, Y., Chen, T. Y., Trister, A. D., Krishnan, R. G., & Mahmood, F. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01567 · Cited by 621 · see citing works

Sequence modeling and design from molecular to genome scale with Evo

Nguyen, E., Poli, M., Durrant, M. G., Kang, B., Katrekar, D., Li, D. B., Bartie, L. J., Thomas, A. W., King, S. H., Brixi, G., et al. (2024) · Science · doi:10.1126/science.ado9336 · Cited by 580 · see citing works

A pathology foundation model for cancer diagnosis and prognosis prediction

Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07894-z · Cited by 580 · see citing works

A foundation model for clinical-grade computational pathology and rare cancers detection

Vorontsov, E., Bozkurt, A., Casson, A., Shaikovski, G., Zelechowski, M., Severson, K., Zimmermann, E., Hall, J., Tenenholtz, N., Fusi, N., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03141-0 · Cited by 572 · see citing works

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

Bandi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Ehteshami Bejnordi, B., Lee, B., Paeng, K., Zhong, A., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2867350 · Cited by 569 · see citing works

The CPTAC Data Portal: A Resource for Cancer Proteomics Research

Edwards, N. J., Oberti, M., Thangudu, R. R., Cai, S., McGarvey, P. B., Jacob, S., Madhavan, S., & Ketchum, K. A. (2015) · Journal of Proteome Research · doi:10.1021/pr501254j · Cited by 565 · see citing works

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Bulten, W., Kartasalo, K., Chen, P.-H. C., Ström, P., Pinckaers, H., Nagpal, K., Cai, Y., Steiner, D. F., van Boven, H., Vink, R., et al. (2022) · Nature Medicine · doi:10.1038/s41591-021-01620-2 · Cited by 559 · see citing works

A deep learning model to predict RNA-Seq expression of tumours from whole-slide images

Schmauch, B., Romagnoni, A., Pronier, E., Saillard, C., Maillé, P., Calderaro, J., Kamoun, A., Sefta, M., Toldo, S., Zaslavskiy, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17678-4 · Cited by 533 · see citing works

DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

Zhang, H., Meng, Y., Zhao, Y., Qiao, Y., Yang, X., Coupland, S. E., & Zheng, Y. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01824 · Cited by 509 · see citing works

A multimodal generative AI copilot for human pathology

Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Zhao, M., Chow, A. K., Ikemura, K., Kim, A., Pouli, D., Patel, A., et al. (2024) · Nature · doi:10.1038/s41586-024-07618-3 · Cited by 461 · see citing works

Perceiver: General Perception with Iterative Attention

Jaegle, A., Gimeno, F., Brock, A., Zisserman, A., Vinyals, O., & Carreira, J. (2021) · arXiv · doi:10.48550/arxiv.2103.03206 · Cited by 414 · see citing works

Multimodal data fusion for cancer biomarker discovery with deep learning

Steyaert, S., Pizurica, M., Nagaraj, D., Khandelwal, P., Hernandez-Boussard, T., Gentles, A. J., & Gevaert, O. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00633-5 · Cited by 402 · see citing works

Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study

Yamashita, R., Long, J., Longacre, T., Peng, L., Berry, G., Martin, B., Higgins, J., Rubin, D. L., & Shen, J. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(20)30535-0 · Cited by 391 · see citing works

CellViT: Vision Transformers for precise cell segmentation and classification

Hörst, F., Rempe, M., Heine, L., Seibold, C., Keyl, J., Baldini, G., Ugurel, S., Siveke, J. T., Grünwald, B. T., Egger, J., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103143 · Cited by 352 · see citing works

Deep learning-based transformation of H&E stained tissues into special stains

Haan, K. D., Zhang, Y., Zuckerman, J. E., Liu, T., Sisk, A. E. J., Diaz, M. F. P., Jen, K., Nobori, A., Liou, S. S., Zhang, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25221-2 · Cited by 326 · see citing works

Deep learning in ultrasound imaging

van Sloun, R. J. G., Cohen, R., & Eldar, Y. C. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2932116 · Cited by 316 · see citing works

Structured crowdsourcing enables convolutional segmentation of histology images

Amgad, M., Elfandy, H., Hussein, H., Atteya, L. A., Elsebaie, M. A. T., Abo Elnasr, L. S., Sakr, R. A., Salem, H. S. E., Ismail, A. F., Saad, A. M., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz083 · Cited by 315 · see citing works

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study

Bilal, M., Raza, S. E. A., Azam, A., Graham, S., Ilyas, M., Cree, I. A., Snead, D., Minhas, F., & Rajpoot, N. M. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00180-1 · Cited by 299 · see citing works

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

Diao, J. A., Wang, J. K., Chui, W. F., Mountain, V., Gullapally, S. C., Srinivasan, R., Mitchell, R. N., Glass, B., Hoffman, S., Rao, S. K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21896-9 · Cited by 281 · see citing works

Federated learning for computational pathology on gigapixel whole slide images

Lu, M. Y., Chen, R. J., Kong, D., Lipkova, J., Singh, R., Williamson, D. F. K., Chen, T. Y., & Mahmood, F. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102298 · Cited by 267 · see citing works

The Genotype-Tissue Expression (GTEx) project

Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263 · see citing works

Deep convolutional neural network for survival analysis with pathological images

Zhu, X., Yao, J., & Huang, J. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822579 · Cited by 258 · see citing works

Interpretable survival prediction for colorectal cancer using deep learning

Wulczyn, E., Steiner, D. F., Moran, M., Plass, M., Reihs, R., Tan, F., Flament-Auvigne, I., Brown, T., Regitnig, P., Chen, P.-H. C., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00427-2 · Cited by 231 · see citing works

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge

Verma, R., Kumar, N., Patil, A., Kurian, N. C., Rane, S. U., Graham, S., Vu, Q. D., Zwager, M. C., Raza, S. E. A., Rajpoot, N., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3085712 · Cited by 228 · see citing works

Swarm learning for decentralized artificial intelligence in cancer histopathology

Saldanha, O. L., Quirke, P., West, N. P., James, J. A., Loughrey, M. B., Grabsch, H. I., Salto-Tellez, M., Alwers, E., Cifci, D., Ghaffari Laleh, N., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01768-5 · Cited by 227 · see citing works

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology

Zhang, D., Schroeder, A., Yan, H., Yang, H., Hu, J., Lee, M. Y. Y., Cho, K. S., Susztak, K., Xu, G. X., Feldman, M. D., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02019-9 · Cited by 219 · see citing works

RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval

Wang, X., Du, Y., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102645 · Cited by 218 · see citing works

Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks

Zeng, Y., Wei, Z., Yu, W., Yin, R., Yuan, Y., Li, B., Tang, Z., Lu, Y., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac297 · Cited by 211 · see citing works

Foundation Model for Cancer Imaging Biomarkers

Pai, S., Bontempi, D., Hadzic, I., Prudente, V., Sokač, M., Chaunzwa, T. L., Bernatz, S., Hosny, A., Mak, R. H., Birkbak, N. J., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00807-9 · Cited by 199 · see citing works

Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer

Shamai, G., Livne, A., Polónia, A., Sabo, E., Cretu, A., Bar-Sela, G., & Kimmel, R. (2022) · Nature Communications · doi:10.1038/s41467-022-34275-9 · Cited by 193 · see citing works

DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

Zhou, Z., Ji, Y., Li, W., Dutta, P., Davuluri, R., & Liu, H. (2023) · arXiv · doi:10.48550/arxiv.2306.15006 · Cited by 179 · see citing works

A hybrid open-top light-sheet microscope for versatile multi-scale imaging of cleared tissues

Glaser, A. K., Bishop, K. W., Barner, L. A. E., Susaki, E. A., Kubota, S. I., Gao, G., Serafin, R., Balaram, P., Turschak, E., Nicovich, P. R., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01468-5 · Cited by 176 · see citing works

Independent real‐world application of a clinical‐grade automated prostate cancer detection system

da Silva, L. M., Pereira, E. M., Salles, P. G., Godrich, R., Ceballos, R., Kunz, J. D., Casson, A., Viret, J., Chandarlapaty, S., Ferreira, C. G., et al. (2021) · The Journal of Pathology · doi:10.1002/path.5662 · Cited by 175 · see citing works

A multimodal whole-slide foundation model for pathology

Ding, T., Wagner, S. J., Song, A. H., Chen, R. J., Lu, M. Y., Zhang, A., Vaidya, A. J., Jaume, G., Shaban, M., Kim, A., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03982-3 · Cited by 171 · see citing works

Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification

Graham, S., Jahanifar, M., Azam, A. S., Nimir, M., Tsang, Y., Dodd, K., Hero, E., Sahota, H., Tank, A., Benes, K., et al. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00082 · Cited by 168 · see citing works

Pathway Pathology

Rosner, A., Miyoshi, K., Landesman‐Bollag, E., Xu, X., Seldin, D. C., Moser, A. R., MacLeod, C. L., Shyamala, G., Gillgrass, A. E., & Cardiff, R. D. (2002) · American Journal Of Pathology · doi:10.1016/s0002-9440(10)64269-1 · Cited by 165 · see citing works

Generative models improve fairness of medical classifiers under distribution shifts

Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S.-A., Tanno, R., Roy, A. G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02838-6 · Cited by 165 · see citing works

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

Jaume, G., Vaidya, A. J., Chen, R. J., Williamson, D. F. K., Liang, P. P., & Mahmood, F. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01100 · Cited by 161 · see citing works

A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics

Hoang, D.-T., Dinstag, G., Shulman, E. D., Hermida, L. C., Ben-Zvi, D. S., Elis, E., Caley, K., Sammut, S.-J., Sinha, S., Sinha, N., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00793-2 · Cited by 157 · see citing works

An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy

Perincheri, S., Levi, A. W., Celli, R., Gershkovich, P., Rimm, D., Morrow, J. S., Rothrock, B., Raciti, P., Klimstra, D., & Sinard, J. (2021) · Modern Pathology · doi:10.1038/s41379-021-00794-x · Cited by 154 · see citing works

Toward Routine Use of 3D Histopathology as a Research Tool

Roberts, N. P., Magee, D. R., Song, Y., Brabazon, K. J., Shires, M., Crellin, D., Orsi, N. M., Quirke, R., Quirke, P., & Treanor, D. E. (2012) · American Journal Of Pathology · doi:10.1016/j.ajpath.2012.01.033 · Cited by 153 · see citing works

One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification

Graham, S., Vu, Q. D., Jahanifar, M., Raza, S. E. A., Minhas, F., Snead, D. R. J., & Rajpoot, N. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102685 · Cited by 151 · see citing works

Vision Transformers for Computational Histopathology

Xu, H., Xu, Q., Cong, F., Kang, J., Han, C., Liu, Z., Madabhushi, A., & Lu, C. Y. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3297604 · Cited by 146 · see citing works

Automated acquisition of explainable knowledge from unannotated histopathology images

Yamamoto, Y., Tsuzuki, T., Akatsuka, J., Ueki, M., Morikawa, H., Numata, Y., Takahara, T., Tsuyuki, T., Tsutsumi, K., Nakazawa, R., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13647-8 · Cited by 145 · see citing works

An integrated iterative annotation technique for easing neural network training in medical image analysis

Lutnick, B. R., Ginley, B. G., Govind, D., McGarry, S. D., LaViolette, P. S., Yacoub, R., Jain, S. R., Tomaszewski, J. E., Jen, K., & Sarder, P. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0018-3 · Cited by 139 · see citing works

Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images

Kalra, S., Tizhoosh, H. R., Choi, C., Shah, S., Diamandis, P., Campbell, C. J. V., & Pantanowitz, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101757 · Cited by 139 · see citing works

A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer

Amgad, M., Hodge, J. M., Elsebaie, M. A. T., Bodelon, C., Puvanesarajah, S., Gutman, D. A., Siziopikou, K. P., Goldstein, J. A., Gaudet, M. M., Teras, L. R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02643-7 · Cited by 137 · see citing works

NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer

Amgad, M. T., Atteya, L. A., Hussein, H., Mohammed, K. H., Hafiz, E. O. A., Elsebaie, M. A., Alhusseiny, A. M., AlMoslemany, M. A., Elmatboly, A. M., Pappalardo, P. A., et al. (2022) · GigaScience · doi:10.1093/gigascience/giac037 · Cited by 136 · see citing works

A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities

Zhao, T., Gu, Y., Yang, J., Usuyama, N., Lee, H. H., Kiblawi, S., Naumann, T., Gao, J., Crabtree, A., Abel, J., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02499-w · Cited by 134 · see citing works

Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study

Kers, J., Bülow, R. D., Klinkhammer, B. M., Breimer, G. E., Fontana, F., Adefidipe, A. A., Hofstraat, R., Corthals, G. L., Sengers, H. P., Djudjaj, S., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00211-9 · Cited by 131 · see citing works

Fast and scalable search of whole-slide images via self-supervised deep learning

Chen, C., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Schaumberg, A. J., & Mahmood, F. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00929-8 · Cited by 130 · see citing works

THItoGene: a deep learning method for predicting spatial transcriptomics from histological images

Jia, Y., Liu, J., Chen, L., Zhao, T., & Wang, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad464 · Cited by 130 · see citing works

Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer

Zhang, Y., Yang, Z., Chen, R., Zhu, Y., Liu, L., Dong, J., Zhang, Z., Sun, X., Ying, J., Lin, D., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01003-0 · Cited by 129 · see citing works

Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides

Saillard, C., Dubois, R., Tchita, O., Loiseau, N., Garcia, T., Adriansen, A., Carpentier, S., Reyre, J., Enéa, D., von Loga, K., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42453-6 · Cited by 126 · see citing works

Deep learning for computational cytology: A survey

Jiang, H., Zhou, Y., Lin, Y., Chan, R., Liu, J., & Chen, H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102691 · Cited by 125 · see citing works

Deep learning for diagnosis and survival prediction in soft tissue sarcoma

Foersch, S., Eckstein, M., Wagner, D., Gach, F., Woerl, A.-C., Geiger, J., Glasner, C., Schelbert, S., Schulz, S., Porubský, Š., et al. (2021) · Annals of Oncology · doi:10.1016/j.annonc.2021.06.007 · Cited by 124 · see citing works

Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning

Lee, Y., Park, J. H., Oh, S., Shin, K., Sun, J., Jung, M., Lee, C., Kim, H., Chung, J.-H., Moon, K. C., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00923-0 · Cited by 124 · see citing works

Merlin: a computed tomography vision–language foundation model and dataset

Blankemeier, L., Kumar, A., Cohen, J., Liu, J., Liu, L., Veen, D. V., Gardezi, S. J. S., Yu, H., Paschali, M., Chen, Z., et al. (2026) · Nature · doi:10.1038/s41586-026-10181-8 · Cited by 124 · see citing works

Color standardization and optimization in Whole Slide Imaging

Yagi, Y. (2011) · Diagnostic Pathology · doi:10.1186/1746-1596-6-s1-s15 · Cited by 123 · see citing works

The hematoxylin and eosin stain in anatomic pathology—An often-neglected focus of quality assurance in the laboratory

Wick, M. R. (2019) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2019.06.003 · Cited by 120 · see citing works

A multi-resolution model for histopathology image classification and localization with multiple instance learning

Li, J., Li, W., Sisk, A. E. J., Ye, H., Wallace, W. D., Speier, W. F., & Arnold, C. W. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104253 · Cited by 118 · see citing works

A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals

Li, D., Bledsoe, J. R., Zeng, Y., Liu, W., Hu, Y., Bi, K., Liang, A., & Li, S. (2020) · Nature Communications · doi:10.1038/s41467-020-19817-3 · Cited by 117 · see citing works

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.18416 · Cited by 117 · see citing works

Multimodal deep learning to predict prognosis in adult and pediatric brain tumors

Steyaert, S., Qiu, Y. L., Zheng, Y., Mukherjee, P., Vogel, H. S., & Gevaert, O. (2023) · Communications Medicine · doi:10.1038/s43856-023-00276-y · Cited by 115 · see citing works

Analysis of 3D pathology samples using weakly supervised AI

Song, A. H., Williams, M., Williamson, D. F. K., Chow, S. S. L., Jaume, G., Gao, G., Zhang, A., Chen, B., Baras, A. S., Serafin, R. J., et al. (2024) · Cell · doi:10.1016/j.cell.2024.03.035 · Cited by 108 · see citing works

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

Jiang, X., Hoffmeister, M., Brenner, H., Muti, H. S., Yuan, T., Foersch, S., West, N. P., Brobeil, A., Jonnagaddala, J., Hawkins, N., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00208-x · Cited by 108 · see citing works

Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationPreprint

Sharma, Y., Shrivastava, A., Ehsan, L., Moskaluk, C. A., Syed, S., & Brown, D. E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.10626 · Cited by 99 · see citing works

Residual cyclegan for robust domain transformation of histopathological tissue slides

de Bel, T., Bokhorst, J., van der Laak, J. A. W. M., & Litjens, G. J. S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102004 · Cited by 95 · see citing works

Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling

Pati, P., Karkampouna, S., Bonollo, F., Compérat, É., Radić, M., Spahn, M., Martinelli, A. L., Wartenberg, M., Julio, M. K., & Rapsomaniki, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00889-5 · Cited by 93 · see citing works

Multimodal contrastive learning for spatial gene expression prediction using histology images

Min, W., Shi, Z., Zhang, J., Wan, J., & Wang, C. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae551 · Cited by 90 · see citing works

Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning

Chan, T. H., Cendra, F. J., Ma, L., Yin, G., & Yu, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01503 · Cited by 87 · see citing works

A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded

Ozyoruk, K. B., Can, S., Darbaz, B., Başak, K., Demir, D., Gokceler, G. I., Serin, G., Hacisalihoglu, U. P., Kurtuluş, E., Lu, M. Y., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00952-9 · Cited by 86 · see citing works

Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors

Li, Z., Cong, Y., Chen, X., Qi, J., Sun, J., Yan, T., Yang, H., Liu, J., Lu, E., Wang, L., et al. (2023) · iScience · doi:10.1016/j.isci.2022.105872 · Cited by 82 · see citing works

Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides

Zhu, M., Ren, B., Richards, R., Suriawinata, M. A., Tomita, N., & Hassanpour, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-86540-4 · Cited by 81 · see citing works

Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning

Bai, B., Wang, H., Li, Y., Haan, K. D., Colonnese, F., Wan, Y., Zuo, J., Doan, N. B., Zhang, X., Zhang, Y., et al. (2022) · BME Frontiers · doi:10.34133/2022/9786242 · Cited by 81 · see citing works

Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

Steinbuß, G., Kriegsmann, M., Zgorzelski, C., Brobeil, A., Goeppert, B. F., Dietrich, S., Mechtersheimer, G., & Kriegsmann, K. (2021) · Cancers · doi:10.3390/cancers13102419 · Cited by 79 · see citing works

PathVQA: 30000+ Questions for Medical Visual Question Answering

He, X., Zhang, Y., Mou, L., Xing, E., & Xie, P. (2020) · arXiv · doi:10.48550/arxiv.2003.10286 · Cited by 79 · see citing works

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Dao, T., & Gu, A. (2024) · arXiv · doi:10.48550/arxiv.2405.21060 · Cited by 76 · see citing works

Deep learning generates synthetic cancer histology for explainability and education

Dolezal, J. M., Wolk, R., Hieromnimon, H. M., Howard, F. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S. E., Kwon, J. W., Agni, M., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00399-4 · Cited by 75 · see citing works

PathAsst: A Generative Foundation AI Assistant towards Artificial General Intelligence of Pathology

Sun, Y., Zhu, C., Zheng, S., Zhang, K., Sun, L., Shui, Z., Zhang, Y., Li, H., & Yang, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28308 · Cited by 74 · see citing works

A comprehensive multi-domain dataset for mitotic figure detection

Aubreville, M., Wilm, F., Stathonikos, N., Breininger, K., Donovan, T., Jabari, S., Veta, M., Ganz, J., Ammeling, J., van Diest, P. J., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02327-4 · Cited by 72 · see citing works

Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics

Haggenmüller, S., Schmitt, M., Krieghoff‐Henning, E., Hekler, A., Maron, R. C., Wies, C., Utikal, J. S., Meier, F. E., Hobelsberger, S., Gellrich, F. F., et al. (2024) · JAMA Dermatology · doi:10.1001/jamadermatol.2023.5550 · Cited by 70 · see citing works

Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning

Hoang, D.-T., Shulman, E. D., Turakulov, R. I., Abdullaev, Z., Singh, O., Campagnolo, E. M., Lalchungnunga, H., Stone, E. A., Nasrallah, M. P., Ruppin, E., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02995-8 · Cited by 69 · see citing works

Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians

DeGrave, A. J., Cai, Z. R., Janizek, J. D., Daneshjou, R., & Lee, S. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01160-9 · Cited by 67 · see citing works

Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions

Alajaji, S. A., Khoury, Z. H., Elgharib, M. A., Saeed, M. M. A., Ahmed, A. R. H., Khan, M. B., Tavares, T., Jessri, M., Puché, A. C., Hoorfar, H., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100369 · Cited by 66 · see citing works

Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology

Mormont, R., Geurts, P., & Marée, R. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2992878 · Cited by 65 · see citing works

Multi-scale representation attention based deep multiple instance learning for gigapixel whole slide image analysis

Xiang, H., Shen, J., Yan, Q., Xu, M., Shi, X., & Zhu, X. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102890 · Cited by 65 · see citing works

HEST-1k: A Dataset For Spatial Transcriptomics and Histology Image Analysis

Jaume, G., Doucet, P., Song, A. H., Lu, M. Y., Almagro-Pérez, C., Wagner, S., Vaidya, A. J., Chen, R. H., Williamson, D. F. K., Kim, A., et al. (2024) · neural information processing systems · doi:10.52202/079017-1704 · Cited by 63 · see citing works

Universal encoding of pan-cancer histology by deep texture representations

Komura, D., Kawabe, A., Fukuta, K., Sano, K., Umezaki, T., Koda, H., Suzuki, R. H., Tominaga, K., Ochi, M., Konishi, H., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110424 · Cited by 62 · see citing works

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

Tolkach, Y., Wolgast, L. M., Damanakis, A. I., Pryalukhin, A. E., Schallenberg, S., Hulla, W., Eich, M., Schroeder, W., Mukhopadhyay, A., Fuchs, M., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00027-4 · Cited by 55 · see citing works

Quilt-1M: One Million Image-Text Pairs for Histopathology

Ikezogwo, W. O., Seyfioglu, M. S., Ghezloo, F., Geva, D. S. C., Mohammed, F. S., Anand, P. K., Krishna, R., & Shapiro, L. (2023) · arXiv · doi:10.48550/arxiv.2306.11207 · Cited by 55 · see citing works

Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Zimmermann, E., Vorontsov, E., Viret, J., Casson, A., Zelechowski, M., Shaikovski, G., Tenenholtz, N., Hall, J., Klimstra, D., Yousfi, R., et al. (2024) · arXiv · doi:10.48550/arxiv.2408.00738 · Cited by 53 · see citing works

Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology

Javed, S. A., Juyal, D., Padigela, H., Taylor-Weiner, A. N., Yu, L., & Prakash, A. (2022) · neural information processing systems · doi:10.52202/068431-1504 · Cited by 52 · see citing works

PyHIST: A Histological Image Segmentation Tool

Muñoz-Aguirre, M., Ntasis, V. F., Rojas, S., & Guigó, R. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008349 · Cited by 51 · see citing works

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 51 · see citing works

Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning

Hinata, M., & Ushiku, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-02168-4 · Cited by 46 · see citing works

Structured State Space Models for Multiple Instance Learning in Digital Pathology

Fillioux, L., Boyd, J., Vakalopoulou, M., Cournède, P.-H., & Christodoulidis, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_57 · Cited by 46 · see citing works

Unstained Tissue Imaging and Virtual Hematoxylin and Eosin Staining of Histologic Whole Slide Images

Koivukoski, S., Khan, U., Ruusuvuori, P., & Latonen, L. (2023) · Laboratory Investigation · doi:10.1016/j.labinv.2023.100070 · Cited by 45 · see citing works

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 45 · see citing works

OCELOT: Overlapped Cell on Tissue Dataset for Histopathology

Ryu, J., Puche, A. V., Shin, J., Park, S., Brattoli, B., Lee, J., Jung, W., Cho, S. I., Paeng, K., Ock, C., et al. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02289 · Cited by 44 · see citing works

2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification

Zhang, J., Nguyen, A. T., Xi, H., Trinh, V. Q., Qin, H., Samaras, D., & Hosseini, M. S. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00339 · Cited by 42 · see citing works

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 41 · see citing works

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 · see citing works

Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning

Su, F., Li, J., Zhao, X., Wang, B., Hu, Y., Sun, Y. L., & Ji, J. (2022) · Laboratory Investigation · doi:10.1038/s41374-022-00742-6 · Cited by 38 · see citing works

ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification

Rymarczyk, D., Pardyl, A., Kraus, J., Kaczyńska, A., Skomorowski, M., & Zieliński, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26387-3_26 · Cited by 37 · see citing works

Diagnostic and Prognostic Value of Protein Post-Translational Modifications in Hepatocellular Carcinoma

Wang, J., Wang, F., Wang, N., Zhang, M., Wang, H., & Huang, G. (2023) · Journal of Clinical and Translational Hepatology · doi:10.14218/jcth.2022.00006s · Cited by 37 · see citing works

Deep 3D histology powered by tissue clearing, omics and AI

Ertürk, A. (2024) · Nature Methods · doi:10.1038/s41592-024-02327-1 · Cited by 37 · see citing works

Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY

Vanea, C., Džigurski, J., Rukins, V., Dodi, O., Siigur, S., Salumäe, L., Meir, K. S., Parks, W. T., Hochner‐Celnikier, D., Fraser, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46986-2 · Cited by 36 · see citing works

Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists

Komura, D., Onoyama, T., Shinbo, K., Odaka, H., Hayakawa, M., Ochi, M., Herdiantoputri, R. R., Endo, H., Katoh, H., Ikeda, T., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100688 · Cited by 35 · see citing works

Artificial Intelligence–Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study

Đačić, S., Travis, W. D., Giltnane, J. M., Kos, F., Abel, J., Hilz, S., Fujimoto, J., Sholl, L. M., Ritter, J. M., Khalil, F. K., et al. (2023) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2023.12.010 · Cited by 35 · see citing works

WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images

Chen, P., Li, H., Zhu, C., Zheng, S., Shui, Z., & Yang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_51 · Cited by 35 · see citing works

Deep Learning of Rhabdomyosarcoma Pathology Images for Classification and Survival Outcome Prediction

Zhang, X., Wang, S., Rudzinski, E. R., Agarwal, S., Rong, R., Barkauskas, D. A., Daescu, O., Cline, L. F., Venkatramani, R., Xie, Y., et al. (2022) · American Journal Of Pathology · doi:10.1016/j.ajpath.2022.03.011 · Cited by 35 · see citing works

Cytomine: Toward an Open and Collaborative Software Platform for Digital Pathology Bridged to Molecular Investigations

Rubens, U., Hoyoux, R., Vanosmael, L., Ouras, M., Tasset, M., Hamilton, C., Longuespée, R., & Marée, R. (2018) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201800057 · Cited by 34 · see citing works

LongNet: Scaling Transformers to 1, 000, 000, 000 Tokens

Ding, J., Ma, S., Dong, L., Zhang, X., Huang, S., Wang, W., Zheng, N., & Wei, F. (2023) · arXiv · doi:10.48550/arxiv.2307.02486 · Cited by 34 · see citing works

The effect of neural network architecture on virtual H&E staining: Systematic assessment of histological feasibility

Khan, U., Koivukoski, S., Valkonen, M., Latonen, L., & Ruusuvuori, P. (2023) · Patterns · doi:10.1016/j.patter.2023.100725 · Cited by 33 · see citing works

Detection of blur artifacts in histopathological whole-slide images of endomyocardial biopsies

Wu, H., Phan, J. H., Bhatia, A. K., Cundiff, C. A., Shehata, B. M., & Wang, M. D. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318465 · Cited by 33 · see citing works

Extending Unsupervised Neural Image Compression With Supervised Multitask LearningPreprint

Tellez, D., Höppener, D. J., Verhoef, C., Grünhagen, D. J., Nierop, P. M. H., Drozdzal, M., van der Laak, J. A. W. M., & Ciompi, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.07041 · Cited by 33 · see citing works

Low-cost and scalable projected light-sheet microscopy for the high-resolution imaging of cleared tissue and living samples

Chen, Y., Chauhan, S., Gong, C., Dayton, H. T., Xu, C., Cruz, E. D. D. L., Tsai, Y.-Y. W., Datta, M. S., Rosoklija, G. B., Dwork, A. J., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01249-9 · Cited by 33 · see citing works

Virtual Staining of Defocused Autofluorescence Images of Unlabeled Tissue Using Deep Neural Networks

Zhang, Y., Huang, L., Liu, T., Cheng, K., Haan, K. D., Li, Y., Bai, B., & Özcan, A. (2022) · Intelligent Computing · doi:10.34133/2022/9818965 · Cited by 32 · see citing works

Untitled work

(2021) · arXiv (Cornell University) · Cited by 31 · see citing works

Development and validation of artificial intelligence-based prescreening of large-bowel biopsies taken in the UK and Portugal: a retrospective cohort study

Bilal, M., Tsang, Y. W., Ali, M., Graham, S., Hero, E., Wahab, N., Dodd, K., Sahota, H., Wu, S., Lu, W., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00148-6 · Cited by 31 · see citing works

Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images

Kanwal, N., López-Pérez, M., Kiraz, U., Zuiverloon, T. C. M., Molina, R., & Engan, K. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102321 · Cited by 31 · see citing works

Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers

Lee, S., Amgad, M. T., Mobadersany, P., McCormick, M., Pollack, B. P., Elfandy, H., Hussein, H., Gutman, D. A., & Cooper, L. (2020) · Cancer Research · doi:10.1158/0008-5472.can-20-0668 · Cited by 30 · see citing works

A diffusion model multi-scale feature fusion network for imbalanced medical image classification research

Zhu, Z., Liu, Y. M., Yuan, C.-A., Qin, X., & Yang, F. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108384 · Cited by 30 · see citing works

Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice

Atallah, N. M., Toss, M. S., Verrill, C. L., Salto‐Tellez, M., Snead, D. R. J., & Rakha, E. A. (2021) · Modern Pathology · doi:10.1038/s41379-021-01000-8 · Cited by 29 · see citing works

CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy

Xia, Y., Zhou, J., Xun, X., Zhang, J., Wei, T., Gao, R., Reddy, B., Liu, C., Kim, G., & Yu, Z. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01784-8 · Cited by 29 · see citing works

Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial

Ramon, A. J., Parmar, C., Carrasco‐Zevallos, O. M., Csiszer, C., Yip, S. S. F., Raciti, P., Stone, N. L., Triantos, S., Quiroz, M. M., Crowley, P., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49153-9 · Cited by 28 · see citing works

RudolfV: A Foundation Model by Pathologists for Pathologists

Dippel, J., Feulner, B., Winterhoff, T., Milbich, T., Tietz, S., Schallenberg, S., Dernbach, G., Kunft, A., Heinke, S., Eich, M.-L., et al. (2024) · arXiv · doi:10.48550/arxiv.2401.04079 · Cited by 27 · see citing works

Multistain Pretraining for Slide Representation Learning in Pathology

Jaume, G., Vaidya, A., Zhang, A., H. Song, A., J. Chen, R., Sahai, S., Mo, D., Madrigal, E., Phi Le, L., & Mahmood, F. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-73414-4_2 · Cited by 27 · see citing works

Multimodal Whole Slide Foundation Model for PathologyPreprint

Ding, T., Wagner, S. J., Song, A. H., Chen, R. J., Lu, M. Y., Zhang, A., Vaidya, A. J., Jaume, G., Shaban, M., Kim, A., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2411.19666 · Cited by 27 · see citing works

Three-dimensional histopathological reconstruction of bladder tumours

Jansen, I., Lucas, M., Savci-Heijink, C. D., Meijer, S. L., Liem, E. I. M. L., de Boer, O. J., van Leeuwen, T. G. J. M., Marquering, H. A., & de Bruin, D. M. (2019) · Diagnostic Pathology · doi:10.1186/s13000-019-0803-7 · Cited by 26 · see citing works

Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer

Zhang, D., Duan, Y., Guo, J., Wang, Y., Yang, Y., Li, Z., Wang, K., Wu, L., & Yu, M. (2022) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2022.3156851 · Cited by 25 · see citing works

Direct image to subtype prediction for brain tumors using deep learning

Hewitt, K. J., Loeffler, C. M. L., Muti, H. S., Berghoff, A. S., Eisenlöffel, C., van Treeck, M., Carrero, Z. I., Nahhas, O. S. M. E., Veldhuizen, G. P., Heuer, S., et al. (2023) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdad139 · Cited by 25 · see citing works

Vim4Path: Self-Supervised Vision Mamba for Histopathology Images

Nasiri-Sarvi, A., Trinh, V. Q., Rivaz, H. J., & Hosseini, M. S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00683 · Cited by 25 · see citing works

Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer

Nimgaonkar, V. U., Krishna, V., Krishna, V., Tiu, E., Joshi, A., Vrabac, D., Bhambhvani, H. P., Smith, K., Johansen, J. S., Makawita, S., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101013 · Cited by 24 · see citing works

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

Ochi, M., Komura, D., Onoyama, T., Shinbo, K., Endo, H., Odaka, H., Kakiuchi, M., Katoh, H., Ushiku, T., & Ishikawa, S. (2024) · Scientific Data · doi:10.1038/s41597-024-03122-5 · Cited by 24 · see citing works

Batch Mode Active Learning on the Riemannian Manifold for Automated Scoring of Nuclear Pleomorphism in Breast Cancer

Das, A., Nair, P. (. ). M. S., & Peter, D. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101805 · Cited by 24 · see citing works

Multi-Cell Type and Multi-Level Graph Aggregation Network for Cancer Grading in Pathology Images

Abbas, S. F., Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2022) · Medical Image Analysis · doi:10.1016/j.media.2023.102936 · Cited by 23 · see citing works

Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining

Chen, F., Zhang, R., Zheng, B., Sun, Y., He, J., & Qin, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_36 · Cited by 23 · see citing works

PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology

Shaikovski, G., Casson, A., Severson, K., Zimmermann, E., Wang, Y. K., Kunz, J. D., Retamero, J. A., Oakley, G., Klimstra, D., Kanan, C., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.10254 · Cited by 22 · see citing works

Virtual Immunohistochemistry Staining for Histological Images Assisted by Weakly-supervised Learning

Li, J., Dong, J., Huang, S., Li, X., Jiang, J., Xiaopeng Fan, B., & Zhang, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01070 · Cited by 20 · see citing works

CellViT: Vision Transformers for Precise Cell Segmentation and ClassificationPreprint

Hörst, F., Rempe, M., Heine, L., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.15350 · Cited by 20 · see citing works

Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs

Kanwal, N., Khoraminia, F., Kiraz, U., Mosquera‐Zamudio, A., Monteagudo, C. C., Janssen, E. A. M., Zuiverloon, T. C. M., Rong, C., & Engan, K. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02676-z · Cited by 19 · see citing works

Phikon-v2, A large and public feature extractor for biomarker predictionPreprint

Filiot, A., Jacob, P. M., Kain, A. M., & Saillard, C. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2409.09173 · Cited by 19 · see citing works

Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

Zaffar, I., Jaume, G., Rajpoot, N., & Mahmood, F. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230723 · Cited by 18 · see citing works

E 2 -MIL: An explainable and evidential multiple instance learning framework for whole slide image classification

Shi, J., Li, C., Gong, T., & Fu, H. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103294 · Cited by 18 · see citing works

Tissue clearing and its applications in human tissues: A review

Mai, H., & Lu, D. (2024) · Open Research (University of Surrey) · doi:10.1002/viw.20230046 · Cited by 18 · see citing works

Deep learning for oncologic treatment outcomes and endpoints evaluation from CT scans in liver cancer

Xia, Y., Zhou, J., Xun, X., Johnston, L. J., Wei, T., Gao, R., Zhang, Y., Reddy, B., Liu, C., Kim, G., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00754-z · Cited by 18 · see citing works

A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma

Zhang, X., Zhao, Z., Wang, R., Chen, H., Zheng, X., Liu, L., Lan, L., Li, P., Wu, S., Cao, Q., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48171-x · Cited by 17 · see citing works

Artifact Restoration in Histology Images with Diffusion Probabilistic Models

He, Z., He, J., Ye, J., & Shen, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_50 · Cited by 16 · see citing works

Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning

Lenz, T., Neidlinger, P., Ligero, M., Wölflein, G., van Treeck, M., & Kather, J. N. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02869 · Cited by 15 · see citing works

3-Dimensional Reconstruction From Histopathological Sections: A Systematic Review

Kurz, A., Müller, H., Kather, J. N., Schneider, L., Bucher, T.-C., & Brinker, T. J. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102049 · Cited by 14 · see citing works

Clinical-Grade Multi-organ Pathology Report Generation for Multi-scale Whole Slide Images via a Semantically Guided Medical Text Foundation Model

Tan, J., Kim, S., Kim, E., Lee, S. H., Ahn, S., & Jeong, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_3 · Cited by 13 · see citing works

Benchmarking foundation models as feature extractors for weakly-supervised computational pathologyPreprint

Neidlinger, P., Nahhas, O. S. M. E., Muti, H. S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H.-M., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.15823 · Cited by 13 · see citing works

PatchSorter: a high throughput deep learning digital pathology tool for object labeling

Walker, C., Talawalla, T., Tóth, R., Ambekar, A., Rea, K., Chamian, O., Fan, F., Berezowska, S., Rottenberg, S., Madabhushi, A., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01150-4 · Cited by 11 · see citing works

Predicting lymph node recurrence in cT1 ‐ 2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists

Adachi, M., Taki, T., Kojima, M., Sakamoto, N., Matsuura, K., Hayashi, R., Tabuchi, K., Ishikawa, S., Ishii, G., & Sakashita, S. (2024) · The Journal of Pathology Clinical Research · doi:10.1002/2056-4538.12392 · Cited by 11 · see citing works

NACNet: A histology context-aware transformer graph convolution network for predicting treatment response to neoadjuvant chemotherapy in Triple Negative Breast Cancer

Li, Q., Teodoro, G., Jiang, Y., & Kong, J. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102467 · Cited by 11 · see citing works

Anatomical foundation models for brain MRIs

Barbano, C. A., Brunello, M., Dufumier, B., & Grangetto, M. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.11.028 · Cited by 11 · see citing works

Scalable, trustworthy generative model for virtual multi-staining from H&E whole slide images

Ounissi, M., Sarbout, I., Hugot, J., Martinez‐Vinson, C., Berrebi, D. D., & Racoceanu, D. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013516 · Cited by 11 · see citing works

A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks

Shafique, A., Gonzalez, R., Pantanowitz, L., Tan, P. H., Machado, A., Cree, I. A., & Tizhoosh, H. R. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100381 · Cited by 10 · see citing works

SegAnyPath: A Foundation Model for Multi- Resolution Stain-Variant and Multi-Task Pathology Image Segmentation

Wang, C., Wan, Y., Li, S., Qu, K., Zhou, X., He, J., Ke, J., Yu, Y., Wang, T., & Shen, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3501352 · Cited by 10 · see citing works

Improving quality control of whole slide images by explicit artifact augmentation

Jurgas, A., Wodziński, M., D’Amato, M., van der Laak, J. A. W. M., Atzori, M., & Müller, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-68667-2 · Cited by 10 · see citing works

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival PredictionPreprint

Jaume, G., Vaidya, A. J., Chen, R., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.06819 · Cited by 10 · see citing works

Crowdsourcing of artificial intelligence algorithms for diagnosis and Gleason grading of prostate cancer in biopsies

Kartasalo, K., Bulten, W., Chen, P.-H. C., Ström, P., Pinckaers, H., Nagpal, K., Pekka, R., Litjens, G. J. S., & Eklund, M. (2022) · European Urology · doi:10.1016/s0302-2838(22)00693-5 · Cited by 9 · see citing works

A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal Cancer

Hacking, S. M., Wu, D., Alexis, C., & Nasim, M. M. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100009 · Cited by 9 · see citing works

Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis

Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works

Evolving and Novel Applications of Artificial Intelligence in Cancer Imaging

Pallumeera, M., Giang, J. C., Singh, R., Pracha, N. S., & Makary, M. S. (2025) · Cancers · doi:10.3390/cancers17091510 · Cited by 29 · see citing works

Deep Learning Approaches for Brain Tumor Classification in MRI Scans: An Analysis of Model Interpretability

Gomes, E. F., & Barbosa, R. S. (2026) · Applied Sciences · doi:10.3390/app16020831 · Cited by 14 · see citing works

Artificial intelligence in hepatology: A comprehensive scoping review of clinical applications, challenges, and future directions

Eskandar, K. (2025) · iLiver · doi:10.1016/j.iliver.2025.100205 · Cited by 13 · see citing works

Supervised-Learning-Driven Interrogation of Organ-on-a-Chip Quality from Microscopy Images

George, R. M., & Kenry, K. (2025) · Chem & Bio Engineering · doi:10.1021/cbe.5c00087 · Cited by 10 · see citing works

Methylation profiling in neuropathological tumors diagnosis: a comprehensive review

Sharie, S. A., Sawaftah, K. A., Qasim, H., & Al-Hussaini, M. A. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1720458 · Cited by 10 · see citing works

Comparative diagnostic accuracy of ChatGPT models in salivary gland disease: a multimodal vignette-based evaluation

Abou‐Bakr, A., Eissa, A. A., alshikh, B. A. A., Ahmed, Y., Shady, E. F. A., Taşsöker, M., & Hassanein, F. E. A. (2025) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-025-09925-5 · Cited by 10 · see citing works

Artificial intelligence-driven pathomics in hepatocellular carcinoma: current developments, challenges and perspectives

Ding, W., Zhang, J.-X., Jin, Z., Hua, H. J., Zu, Q., Yang, S., Wang, W., Liu, S., Zhou, H.-F., & Shi, H.-B. (2025) · Discover Oncology · doi:10.1007/s12672-025-03254-z · Cited by 10 · see citing works

Explainability and Trust in Deep Learning for Cancer Imaging: Systematic Barriers, Clinical Misalignment, and a Translational Roadmap

Borra, S., Dey, N., Fong, S., Sherratt, R. S., & Shi, F. (2026) · Cancers · doi:10.3390/cancers18091361 · Cited by 8 · see citing works

Digital Pathology with AI for Cervical Biopsies: Diagnostic Accuracy at the CIN2+ Threshold

Andreassen, A. K., Mortensen, E., Stenbro, R., Sørensen, Ø., & Sørbye, S. W. (2025) · Cancers · doi:10.3390/cancers17233808 · Cited by 5 · see citing works

AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue

Sakaguchi, M., Yoshizawa, A., Masui, K., Sakai, T., & Komori, T. (2025) · Cancers · doi:10.3390/cancers18010009 · Cited by 6 · see citing works

PatchSight–ImmuneMap–LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction

Al-Nussairi, A. K. J., Ali, A. B. M., Malik, S., Patro, S. G. K., Mahanty, C., Abass, K. S., Basheti, I. A., Dessalegn, A. A., Muzammil, K., & Kumar, S. (2026) · Discover Oncology · doi:10.1007/s12672-026-04491-6 · Cited by 5 · see citing works

Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence

Alwahaibi, N. (2026) · Biomedicines · doi:10.3390/biomedicines14020407 · Cited by 5 · see citing works

A scoping review of TSR analysis in colorectal cancer: implications for automated solutions

Dikland, F. A., Fekih, C., Wellenstein, M. R. J., da Silva, R. S., Machado-Neves, R., Fraga, J., Oliveira, D., Montezuma, D., Pinto, I., & Woodburn, J. (2025) · Oncology Reviews · doi:10.3389/or.2025.1605383 · Cited by 4 · see citing works

Optimized deep learning-based multi-stage Alzheimer’s disease characterization with temporal modeling and soft-NMS for neuroimaging

Nawaz, K., Zanib, A., Wang, Y., Ayesha, N., Bahaj, S. A., & Mahmood, T. (2026) · Discover Computing · doi:10.1007/s10791-026-09938-6 · Cited by 4 · see citing works

Quantum Machine Learning in Medical Image Analysis: From Diagnostics to Surgery Planning

Sar, A., Kumar, A., Roy, S., Kaushish, A., Choudhury, T., Minakshi, Alqahtani, S. H., & Abraham, A. (2026) · IEEE Access · doi:10.1109/access.2026.3663498 · Cited by 7 · see citing works

A roadmap for AI-aided digital twins in computational nuclear oncology: A position paper by the EANM

Cavinato, L., Kirienko, M., Papp, L., Shi, K., Visvikis, D., Chiti, A., & Sollini, M. (2025) · The EANM Journal · doi:10.1016/j.eanmj.2025.100007 · Cited by 4 · see citing works

Advancements in bone marrow biopsy: the role of omics and artificial intelligence in hematologic diagnostics

Alwahaibi, M., & Alwahaibi, N. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1772478 · Cited by 3 · see citing works

Artificial Intelligence in Medicine: Basic Knowledge, Applications, Ethics and Perspectives

Cascella, M., Avella, M. T., Pumpo, M. D., Sebastiani, E., Bussa, M., Tagliaferri, A., Brunetti, B., Meloni, P., Bianchini, E., Graziani, P., et al. (2026) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.70484 · Cited by 5 · see citing works

Multimodal data analysis reveals asynchronous aging dynamics across female reproductive organs

Soldatkina, O., Pedro, L. V.-S., Pujol‐Gualdo, N., Hommad, A. E., Ramírez, J. M., Ripoll-Cladellas, A., Sopena-Rios, M., Tabares, D., Pérez-Elena, M. Á., Torrents, D., et al. (2026) · Nature Aging · doi:10.1038/s43587-026-01098-y · Cited by 2 · see citing works

Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities

Vela, V., Sonay, A. Y., Limani, P., Graf, L., Sabani, B., Gjermeni, D., Rroku, A., Zela, A., Gorica, E., Biefer, H. R. C., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14217555 · Cited by 2 · see citing works

Renal Cell Carcinoma subtyping: Learning from multi-resolution localization

Mohamad, M., Ponzio, F., Cataldo, S. D., Ambrosetti, D., & Descombes, X. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109155 · Cited by 2 · see citing works

GEM-Net: a compact Ghost-ECA CNN with multi-stage feature fusion for multi-domain histopathology classification

Hasan, M. J., Ahmad, W. S. H. M. W., Fauzi, M. F. A., Abas, F. S., Cheah, P., Chiew, S.-F., & Looi, L. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00490-3 · Cited by 2 · see citing works

Beyond algorithmic performance: translational gaps in implementing artificial intelligence for clinical digital pathology

Xia, R., Littlefield, N. G., Bao, R., & Gu, Q. (2026) · Journal of Histotechnology · doi:10.1080/01478885.2026.2630525 · Cited by 2 · see citing works

Structure-Preserving Histopathological Stain Normalization via Attention-Guided Residual Learning

Madusanka, N., Padmanabha, P., Guruge, K., & Lee, B.-I. (2025) · Bioengineering · doi:10.3390/bioengineering12090950 · Cited by 2 · see citing works

Comparative Evaluation of Fusion Strategies Using Multi-Pretrained Deep Learning Fusion-Based (MPDLF) Model for Histopathology Image Classification

Alshohoumi, F., & Al-Hamdani, A. (2026) · Applied Sciences · doi:10.3390/app16041964 · Cited by 2 · see citing works

Preclinical Insights into Wound Healing: Models, Mechanisms, and Translational Strategies

Jeyaraman, N., Roy, M., Muthu, S., Sharun, K., & Jeyaraman, M. (2026) · Stem cell biology and regenerative medicine · doi:10.1007/978-3-032-12442-5_12 · Cited by 1 · see citing works

MOSAIC: Multi-scale orientation-aware segmentation and instance classification network for histopathological image analysis

Wadood, A. S., Fauzi, M. F. A., Wong, L.-K., Lee, J. T. H., Khor, S. Y., & Looi, L. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109334 · Cited by 1 · see citing works

Artificial intelligence–Assisted tissue classification in Chironomus riparius : A potential tool for ecotoxicological studies

Stojanović, J., Milosavljević, A., Savić‐Zdravković, D., & Milošević, D. (2026) · Environmental Toxicology and Chemistry · doi:10.1093/etojnl/vgag059 · Cited by 1 · see citing works

Advancement of deep learning models with whole slide image in diagnosis, subtyping and prognosis for glioma

Gui, K., Li, Q., Xie, Z. Y., Zhan, T., Long, X., Yang, G., Luo, K., & Liu, H. (2026) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ae7860 · Cited by 1 · see citing works

A CLIP-based framework for multiclass lung histopathology classification with prompt engineering and class-imbalance-aware focal optimization

Munawar, S., Hanif, F., Raza, A., Farman, M. T., & Hınçal, E. (2026) · Scientific Reports · doi:10.1038/s41598-026-55588-5 · Cited by 1 · see citing works

Optimizing Deep Learning Models for Ovarian Cancer Subtype Classification: A Systematic Evaluation of Architectures and Data Augmentation StrategiesPreprint

Zhou, D., Zhang, J., Ma, J., Xi, X., & Ma, R. (2025) · Research Square · doi:10.21203/rs.3.rs-6216837/v1

Non-segmented unsupervised learning of multispectral whole slide images for robust analysis of tissue repair and regenerationPreprint

Mansfield, K. P., Mestvirishvili, T., Subhan, B. S., Mezzano, V., Almanzar, D. A., Hanson, S., Tan, J., Loomis, C. A., Fenyö, D., Tsirigos, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.19.671136

Congenital and Neonatal Skin Disorders: Histopathological Diagnosis and Syndromic Associations

Qasim, H., Shugaer, M. A., Khattab, K., Leoni, M. L. G., & Varrassi, G. (2025) · Cureus · doi:10.7759/cureus.90102

Diagnostic Performance of Self-Supervised Foundation Models for Intraoperative Quantification of Hepatic Macrovesicular SteatosisPreprint

Koga, S., Guda, A., Wang, Y., Sahni, A., Wu, J. Z., Rosen, A., Nield, J., Nandish, N., Patel, K., Goldman, H. M., et al. (2025) · medRxiv · doi:10.1101/2025.09.16.25335833

Optimized EfficientNet for Detection of Endometrial Cancer Using Histopathological Images

Keerthishree, B. T., & Ravikumar, P. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.13925

CardioPulmoNet: Modeling Cardiopulmonary Dynamics for Histopathological DiagnosisPreprint

Pham, T. D. (2026) · SSRN Electronic Journal · doi:10.64898/2026.02.19.26346620

Automated Squamous Cell Carcinoma Diagnosis using Suppor Vector Machine and Convolution Neural Network

Kaur, J., Gupta, V. K., Aggarwal, S., Tyagi, V., Upadhyay, A., & Meena, J. (2025) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit66168.2025.11400039

Artificial Intelligence in Lung Cancer Management: Innovations, Challenges, and Clinical Translation

Jiang, Q., Qiu, J., & KarkonShayan, F. (2026) · Neural Processing Letters · doi:10.1007/s11063-026-11836-3

Multi-Framework Evaluation for Breast Cancer Classification: From CNNs to Swin Transformer

Athilakshmi, R., Maragatham, G., Tamilselvan, M., -, S. M., Karthikeyan, V. A., & Ravi, H. (2026) · International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect68478.2026.11426123

Unsupervised feature extraction with modern deep learning approaches determines tissue specific clustering performance

Lewis, S. A., Pellegrino, A., Brandwein‐Weber, M., Brandwein, A., Comess, S. R., Sayeed, S., Yoon, M. J., Wiedmer, C., Moraes, I., Sohn, S., et al. (2026) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3088220

Assessing CNNs and LoRA-Fine-Tuned Vision–Language Models for Breast Cancer Histopathology Image Classification

Zhaksylyk, T., Abdikenov, B., Saidnassim, N., Ayanbayev, B., Imasheva, A., & Karibekov, T. (2026) · Journal of Imaging · doi:10.3390/jimaging12040168

Multimodal medical image reconstruction and organ-wise disease classification using a hybrid deep learning-Kalman filtering framework

Arif, S. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1770289

Automated model of skeletal muscle atrophy from H&E-stained biopsy images using deep learning

Cai, H., Yang, Q., Li, Y., Liu, J., Xu, J., & Yu, X. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae62d9

Decoding Cancer Tissues: A Comparative Deep Learning View of Breast Histopathology

Malik, S., Khetan, M., Oberoi, R., & Vankayalapati, H. D. (2026) · IEEE International Conference on AI Engineering and Innovations (AIEI) · doi:10.1109/aiei69164.2026.11497604

GAN-Enhanced Deformable Deep Learning Architecture for Accurate Spine Abnormality Detection

Rajathi, S., Rubha, M., Arthi, P. M., Saravanan, T., Santhoshkumar, S. P., & Kaliappan, A. (2026) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict68280.2026.11510768

Standardized Instance‐Level Quantification of CD34‐Positive Vessels in Lymph Node Whole‐Slide Images Using U‐Net

Omotani, S., Yonemoto, N., Muramoto, A., & Kobayashi, M. (2026) · Pathology International · doi:10.1111/pin.70125

Ligand–Receptor Interaction Combined with Histopathology Improves Glioma Prognostic Model

Gao, L., Zhang, R., Zhu, X., Xu, H. X., Chen, Q., Peng, M., & Liu, J. (2026) · Biomedicines · doi:10.3390/biomedicines14051110

Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology

Alwahaibi, N. (2026) · Histochemistry and Cell Biology · doi:10.1007/s00418-026-02490-w · Cited by 2 · see citing works

Advances in the detection of deadly free-living amoebae (FLA)

Pandit, S., Hazra, S., Dinda, S. K., Bhattacharjee, B., Basu, A., Pradhan, B., Kumar, K., & Manna, D. (2026) · Diagnostic Microbiology and Infectious Disease · doi:10.1016/j.diagmicrobio.2026.117465

STING organelle stress sensing in the liver drives hepatocyte fate and remodeling

Hamad, R. S., Elmorsy, E. A., Elnaghy, F., Amer, M. M., Abdel-Hamed, M. R., Farrag, A. A., Eissa, H., El-kott, A. F., Negm, S., AlShehri, M. A., et al. (2026) · Tissue and Cell · doi:10.1016/j.tice.2026.103629

Histology image sequence representation using a deep generative neural network

Thulasidharan, P. P., Mary, L., Nath, K., & Sahoo, J. (2026) · ETRI Journal · doi:10.4218/etrij.2025-0494

Machine learning in the diagnosis of Hirschsprung disease: a systematic review and meta-analysis

Liu, H. Y., Zhou, Y., Wang, X., Wang, C. Y., Guo, S., Sun, M., Li, S., & Wang, Y. (2026) · Pediatric Surgery International · doi:10.1007/s00383-026-06487-3

Unraveling the complexity of gynecological cancers: challenges, resistance, and the road to precision medicine - A Narrative Review

Wolde, T., & Belay, M. (2026) · Advances in Cancer Biology - Metastasis · doi:10.1016/j.adcanc.2026.100195

Evaluation of Machine Learning Methods for Oral Cavity Histopathological Cancer Classification in a Brazilian Cohort

Baffa, M. D. F. O., Bachmann, L., Zezéll, D. M., Matos, L. L., & Felipe, J. C. (2026) · doi:10.5753/sbcas.2026.20718

Artificial intelligence for breast cancer stem cell characterization imaging and diagnostics

Iqbal, G., Kumari, K., Sahu, N., Qayoom, H., & Mir, M. F. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-49109-2.00013-4

Beyond one stain: Cross-Modality feature learning boosts accuracy in HER2 status scoring

Oyelade, O. N., Hoxey, O., & Humrye, Y. (2026) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2026.101785

Pathological image segmentation using bi-level differentiable super token attention-based neural architecture search

Du, B., Huang, L., Liu, X., & Yang, T. (2026) · Discover Computing · doi:10.1007/s10791-026-10364-x

Reducing False Negatives in AI-Based Breast Histopathology: A Clinically Oriented Evaluation of Deep Learning Models Under Domain Shift

Stănescu, L., & Spahiu, C. S. (2026) · Diagnostics · doi:10.3390/diagnostics16152371

Количественная оценка результатов иммуногистохимического исследования: современные подходы и ограничения метода

Shcheglov, B. O., Ким, А. Р., Рева, И. В., Золотов, А. С., Александрович, К. Ю., Shcheglova, S. N., Gainullina, Y., Усов, В. В., & Reva, G. V. (2026) · Clinical and Fundamental Medicine · doi:10.24866/3033-5485/2026-2/19-35

NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation

Torbati, N., Meshcheryakova, A., Woitek, R., Hatamikia, S., Mechtcheriakova, D., & Mahbod, A. (2026) · Bioengineering · doi:10.3390/bioengineering13080886

Efficient colon cancer diagnosis (CCD) strategy based on hybrid deep and machine learning techniques

Khalifa, H. R., Rabie, A. H., Amer, H. M., Saleh, A. I., & Abo-Elsoud, M. E. A. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133909

Spatially resolved tissue architecture and computational pathology in pancreatic cancer

Bae, S.-W., Tsirigos, A., Min, J., & Maitra, A. (2026) · Experimental & Molecular Medicine · doi:10.1038/s12276-026-01782-4

Toward AI-Assisted Precision Diagnostics in Breast Cancer: Source-Group-Preserving Evaluation of Class-Imbalance Strategies for Ordered ER-IHC Segmentation

Arefin, M. S., Islam, M. S., Nabi, M. S., Sumon, R. I., Lapina, M., Lapin, V. G., & Muthanna, M. S. A. (2026) · Diagnostics · doi:10.3390/diagnostics16172752

Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps

Fathima, J. H. S., Narendran, M. R., Quadri, M. S. A., Vinothkumar, T. S., Kamala, K., & Karobari, M. I. (2026) · Analytical Cellular Pathology · doi:10.1155/ancp/9281281

Distance-guided multi-field-of-view attention for intelligent tumour segmentation in histopathology images

Kahttana, I., Ilyas, T., Kim, J., & Lee, S. J. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.134211

Lightweight deep learning models for histopathological image analysis in resource-constrained healthcare environments

Ahmed, A. A., Adeshina, S. A., Aleshinloye, Y. A., Ogundokun, R. O., & Owolawi, P. A. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100475

Development and evaluation of a deep learning framework for screening of cervical lymph node metastasis in microscopic images of oral squamous cell carcinoma: A proof of concept study

Basu, S. N., Augustine, J., Chaudhary, N., Ahmad, T., Khurana, N., & Urs, A. B. (2026) · Journal of Microscopy · doi:10.1111/jmi.70170

Sudden cardiac death: forensic investigation and its implications for cardiovascular risk stratification and prevention

Sessa, F., Zuccarello, P., Francaviglia, M., Chisari, M., Esposito, M., Mauro, L. D., Dervišević, E., Pomara, C., & Salerno, M. (2026) · Vessel Plus · doi:10.20517/2574-1209.2026.53

Machine Learning Techniques for Histological Tissue Classification in Veterinary Science

Joshaline, D. C. M., Kesavardhini, K., Saranraj, P., Dayana, S., Tabassum, R. S. R., & Lokeshwari, B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1358-8.ch002

Emerging Advances in Tissue Classification Using AI in Veterinary Histopathology

Gharban, H. A. J., Gayathri, K. V., Manigandan, M., Singh, P., Mead, A. O., & Logesan, Y. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1358-8.ch006

An Explainable Patch-Based Deep Learning Framework for Marker-Specific Computational Assessment of Ovarian Autophagy in Digital Histopathology

Kılıçkaya, F. N., Öztürk, C., Köseoğlu, E., & Arzu Hanım, Y. A. Y. (2026) · Bioengineering · doi:10.3390/bioengineering13101103

Enhancing Oral Leukoplakia Segmentation in Histopathological Images Through Heterogeneous Deep Learning Ensemble with Soft Voting

Saar, L. N. D. F. B., Silva, A. B., Oliveira, D. L. L., Neves, L. A., Oliveira, P. A. Á., Martins, A. S., Tosta, T. A. A., de Faria, P. R., & do Nascimento, M. Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39895-6_33

Artificial intelligence in digital pathology: Principles, applications, challenges, and future directions

Wang, Y., & Odabachian, L. (2026) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2026.100749

AI driven oral cancer diagnosis from clinical imaging and computational pathology to precision therapeutics

Agarwal, S., Das, B., Singh, S., & Gupta, B. P. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02573-6

Artificial Intelligence-Assisted Quantification in Translational Breast Cancer-Related Lymphedema Research (Preprint)Preprint

Konig, D. J., Feguson-Nguyen, P., Mohan, G., Calabrese, K., Ahmed, S., Sullivan, S., Subi-Kasozi, C. A., Li, R. Y., Jorge, M., Sinha, M., et al. (2026) · doi:10.2196/preprints.114930

Machine Learning Methods for Histopathological Image Analysis

Komura, D., & Ishikawa, S. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.001 · Cited by 937 · see citing works

Automated Immunohistochemistry Scoring: A Review of Advanced Computational Methods in Histopathology

Petríková, D., & Cimrák, I. (2025) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct67853.2025.11239232

Machine learning in computational histopathology: Challenges and opportunities

Cooper, M. C., Ji, Z., & Krishnan, R. G. (2023) · Genes Chromosomes and Cancer · doi:10.1002/gcc.23177 · Cited by 81 · see citing works

The Quest for the Application of Artificial Intelligence to Whole Slide Imaging: Unique Prospective from New Advanced Tools

Faa, G., Castagnola, M., Didaci, L., Coghe, F., Scartozzi, M., Saba, L., & Fraschini, M. (2024) · Algorithms · doi:10.3390/a17060254 · Cited by 14 · see citing works

Artificial intelligence applications in histopathology

Bahadir, C. D., Omar, M., Rosenthal, J., Marchionni, L., Liechty, B., Pisapia, D. J., & Sabuncu, M. R. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-023-00012-7 · Cited by 84 · see citing works