Machine Learning Methods for Histopathological Image Analysis
Open accessAbstract
Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.
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In histopathology, variation between hospitals can arise from differences in the patient population or in slide staining and image acquisition.
“In histopathology applications-studying tissue slides under a microscope-this variation can arise from sources like differences in the patient population or in slide staining and image acquisition (Veta et al., 2016; Komura and Ishikawa, 2018; Tellez et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Komura and Ishikawa, 2018
The “Color Variation and Artifacts” section identifies staining conditions and scanner models as sources of variation in histopathological images. It does not identify differences in patient populations as a source of variation between hospitals, so that part of the attributed claim is unsupported. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn histopathology applications, variation can arise from sources like differences in the patient population or in slide staining and image acquisition.
“In histopathology applications-studying tissue slides under a microscope-this variation can arise from sources like differences in the patient population or in slide staining and image acquisition (Veta et al., 2016; Komura and Ishikawa, 2018; Tellez et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Komura and Ishikawa, 2018
The “Color Variation and Artifacts” section identifies staining reagents, staining conditions, tissue-section thickness, and scanner models as sources of variation in histopathological images. It does not identify differences in patient population as a source, so that part of the attributed claim is unsupported by this paper. (LLM, conf=0.88)
Experimental — accuracy not yet measured
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Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces
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An Active Learning Approach for Rapid Characterization of Endothelial Cells in Human Tumors
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Scalable Histopathological Image Analysis via Active Learning
Zhu, Y., Zhang, S., Liu, W., & Metaxas, D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10443-0_47 · Cited by 21 · see citing works
Histopathological Image QTL Discovery of Immune Infiltration Variants
Barry, J. D., Fagny, M., Paulson, J. N., Aerts, H. J. W. L., Platig, J., & Quackenbush, J. F. (2018) · iScience · doi:10.1016/j.isci.2018.07.001 · Cited by 21 · see citing works
Automated Training Data Generation for Microscopy Focus Classification
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Intraoperative neuropathology of glioma recurrence: cell detection and classification
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Automatic Lymphocyte Detection in H&E Images with Deep Neural NetworksPreprint
Chen, J., & Srinivas, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03217 · Cited by 14 · see citing works
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Automatic breast cancer grading in lymph nodes using a deep neural networkPreprint
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A Brief History of the Discovery of Helicobacter pylori
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A discriminant multi-scale histopathology descriptor using dictionary learning
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Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
Digital pathology and artificial intelligence
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Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging
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Molecular and histological correlations in liver cancer
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Artificial intelligence and computational pathology
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Artificial intelligence and machine learning for medical imaging: A technology review
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Accurate brain tumor detection using deep convolutional neural network
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Tutorial: guidance for quantitative confocal microscopy
Jonkman, J. E. N., Brown, C. M., Wright, G. D., Anderson, K. I., & North, A. J. (2020) · Nature Protocols · doi:10.1038/s41596-020-0313-9 · Cited by 404 · see citing works
Pathology image analysis using segmentation deep learning algorithms
Wang, S., Yang, D. M., Rong, R., Zhan, X., & Xiao, G. (2019) · The American Journal of Pathology · doi:10.1016/j.ajpath.2019.05.007 · Cited by 382 · see citing works
Artificial intelligence in oncology
Shimizu, H., & Nakayama, K. I. (2020) · Cancer Science · doi:10.1111/cas.14377 · Cited by 388 · see citing works
Salvi, M., Acharya, U. R., Molinari, F., & Meiburger, K. M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104129 · Cited by 399 · see citing works
Digital Pathology: Advantages, Limitations and Emerging Perspectives
Jahn, S. W., Plass, M., & Moinfar, F. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9113697 · Cited by 351 · see citing works
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Howard, F. M., Dolezal, J., Kochanny, S., Schulte, J., Chen, H., Heij, L., Huo, D., Nanda, R., Olopade, O. I., Kather, J. N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24698-1 · Cited by 330 · see citing works
Çelik, Y., Talo, M., Yıldırım, Ö., Karabatak, M., & Acharya, U. R. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.03.011 · Cited by 322 · see citing works
Self supervised contrastive learning for digital histopathology
Ciga, O., Xu, T., & Martel, A. L. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100198 · Cited by 294 · see citing works
Li, X., Li, C., Rahaman, M. M., Sun, H., Li, X., Wu, J., Yao, Y., & Grzegorzek, M. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10121-0 · Cited by 291 · see citing works
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
“In histopathology applications—studying tissue slides under a microscope—this variation can arise from sources like differences in the patient population or in slide staining and image acquisition (Veta et al., 2016; Komura and Ishikawa, 2018; Tellez et al., 2019).”
Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling
Hirra, I., Ahmad, M., Hussain, A., Ashraf, M. U., Saeed, I. A., Qadri, S. F., Alghamdi, A. M., & Alfakeeh, A. S. (2021) · IEEE Access · doi:10.1109/access.2021.3056516 · Cited by 277 · see citing works
Deep learning for colon cancer histopathological images analysis
Hamida, A. B., Devanne, M., Weber, J., Truntzer, C., Dérangère, V., Ghiringhelli, F., Forestier, G., & Wemmert, C. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104730 · Cited by 253 · see citing works
Artificial Intelligence in Pathology
Chang, H. Y., Jung, C. K., Woo, J. I., Lee, S.-H., Cho, J., Kim, S. W., & Kwak, T.-Y. (2018) · Journal of Pathology and Translational Medicine · doi:10.4132/jptm.2018.12.16 · Cited by 249 · see citing works
Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice
Colling, R. T., Pitman, H., Oien, K. A., Rajpoot, N., Macklin, P. S., CM‐Path AI in Histopathology Working Group, Snead, D. R. J., Sackville, T., & Verrill, C. L. (2019) · The Journal of Pathology · doi:10.1002/path.5310 · Cited by 238 · see citing works
Tang, Z., Chuang, K. V., DeCarli, C. S., Jin, L., Beckett, L. A., Keiser, M. J., & Dugger, B. N. (2019) · Nature Communications · doi:10.1038/s41467-019-10212-1 · Cited by 228 · see citing works
Tomita, N., Abdollahi, B., Wei, J., Ren, B., Suriawinata, A., & Hassanpour, S. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.14645 · Cited by 204 · see citing works
Gertych, A., Świderska-Chadaj, Ż., Ma, Z., Ing, N., Markiewicz, T., Cierniak, S., Salemi, H., Guzman, S. J., Walts, A. E., & Knudsen, B. S. (2019) · Scientific Reports · doi:10.1038/s41598-018-37638-9 · Cited by 189 · see citing works
Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs
Endres, M. G., Hillen, F., Salloumis, M., Sedaghat, A. R., Niehues, S. M., Quatela, O. E., Hanken, H., Smeets, R., Beck-Broichsitter, B. E., Rendenbach, C., et al. (2020) · Diagnostics · doi:10.3390/diagnostics10060430 · Cited by 183 · see citing works
High-accuracy prostate cancer pathology using deep learning
Tolkach, Y., Dohmgörgen, T., Toma, M. I., & Kristiansen, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0200-7 · Cited by 183 · see citing works
A Deep Learning Method for Breast Cancer Classification in the Pathology Images
Liu, M., Hu, L., Tang, Y. G., Wang, C., He, Y., Zeng, C., Lin, K., He, Z., & Huo, W. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3187765 · Cited by 178 · see citing works
Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence
Wang, K., Yu, G., Xu, C., Meng, X., Zhou, J., Zheng, C., Deng, Z., Shang, L., Liu, R., Su, S., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-01942-5 · Cited by 191 · see citing works
Artificial Intelligence for the Otolaryngologist: A State of the Art Review
Bur, A. M., Shew, M. A., & New, J. (2019) · Otolaryngology · doi:10.1177/0194599819827507 · Cited by 172 · see citing works
Cellular community detection for tissue phenotyping in colorectal cancer histology images
Javed, S., Mahmood, A., Fraz, M. M., Koohbanani, N. A., Benes, K., Tsang, Y., Hewitt, K. J., Epstein, D., Snead, D. R. J., & Rajpoot, N. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101696 · Cited by 171 · see citing works
Prognostic and predictive value of a pathomics signature in gastric cancer
Chen, D., Fu, M., Chi, L., Lin, L., Cheng, J., Xue, W., Long, C., Jiang, W., Dong, X., Sui, J. H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34703-w · Cited by 164 · see citing works
Hierarchical graph representations in digital pathology
Pati, P., Jaume, G., Foncubierta–Rodríguez, A., Feroce, F., Anniciello, A. M., Scognamiglio, G., Brancati, N., Fiche, M., Dubruc, E., Riccio, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102264 · Cited by 168 · see citing works
Deep Learning-Based Classification of Liver Cancer Histopathology Images Using Only Global Labels
Sun, C., Xu, A., Liu, D., Xiong, Z., Zhao, F., & Ding, W. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2949837 · Cited by 157 · see citing works
Hong, R., Liu, W., DeLair, D., Razavian, N., & Fenyö, D. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100400 · Cited by 154 · see citing works
Sun, H., Zeng, X., Xu, T., Peng, G., & Ma, Y. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2944977 · Cited by 152 · 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
“In total, we experiment with four variations of TITAN and present the average performance over four challenging subtyping tasks, TCGA-UT-8K, TCGA-OT, OT108 and EBRAINS (individual results can be found in Supplementary Tables 110–113).”
Self-citationAccurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
Yu, G., Sun, K., Xu, C., Shi, X.-H., Wu, C., Xie, T., Meng, R.-Q., Meng, X.-H., Wang, K.-S., Xiao, H.-M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26643-8 · Cited by 147 · see citing works
Dzobo, K., Adotey, S. K., Thomford, N. E., & Dzobo, W. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0038 · Cited by 150 · see citing works
The future of pathology is digital
Pallua, J. D., Brunner, A., Zelger, B., Schirmer, M., & Haybaeck, J. (2020) · Pathology - Research and Practice · doi:10.1016/j.prp.2020.153040 · Cited by 141 · see citing works
An enhanced multimodal fusion deep learning neural network for lung cancer classification
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Machine learning approaches for pathologic diagnosis
Komura, D., & Ishikawa, S. (2019) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-019-02594-w · Cited by 143 · see citing works
Self-citationAutomated 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
Cardiac tissue engineering: state-of-the-art methods and outlook
Nguyen, A. H., Marsh, P., Schmiess-Heine, L., Burke, P. J., Lee, A. P., Lee, J., & Cao, H. (2019) · Journal of Biological Engineering · doi:10.1186/s13036-019-0185-0 · Cited by 142 · 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
Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Barros, D. M. S., Moura, J. C. C., Freire, C. R., Taleb, A. C., Valentim, R. A. D. M., & de Morais, P. S. G. (2020) · BioMedical Engineering OnLine · doi:10.1186/s12938-020-00767-2 · Cited by 140 · see citing works
The Xception model: A potential feature extractor in breast cancer histology images classification
Sharma, S., & Kumar, S. (2021) · ICT Express · doi:10.1016/j.icte.2021.11.010 · Cited by 137 · see citing works
A bird's-eye view of deep learning in bioimage analysis
Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126 · see citing works
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Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer
Kriegsmann, M., Haag, C., Weis, C., Steinbuß, G., Warth, A., Zgorzelski, C., Muley, T., Winter, H., Eichhorn, M., Eichhorn, F., et al. (2020) · Cancers · doi:10.3390/cancers12061604 · Cited by 136 · see citing works
Rastogi, P., Khanna, K., & Singh, V. P. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105236 · Cited by 131 · see citing works
Ščupáková, K., Balluff, B., Tressler, C. M., Adelaja, T., Heeren, R. M. A., Glunde, K., & Ertaylan, G. (2019) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2019-0858 · Cited by 129 · 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
Rączkowski, Ł., Możejko, M., Zambonelli, J., & Szczurek, E. (2019) · Scientific Reports · doi:10.1038/s41598-019-50587-1 · Cited by 122 · see citing works
Mahbod, A., Schaefer, G., Bancher, B., Löw, C., Dorffner, G., Ecker, R., & Ellinger, I. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104349 · Cited by 122 · see citing works
Computational pathology: A survey review and the way forward
Hosseini, M. S., Bejnordi, B. E., Trinh, V. Q., Chan, L., Hasan, D., Li, X., Yang, S. C., Kim, T., Zhang, H., Wu, T., et al. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100357 · Cited by 124 · see citing works
Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning
Syrykh, C., Abreu, A., Amara, N., Siegfried, A., Maisongrosse, V., Frénois, F.-X., Martin, L., Rossi, C., Laurent, C., & Brousset, P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0272-0 · Cited by 116 · see citing works
Álvarez-Alvarado, J. M., Ríos-Moreno, J. G., Biosca, S. A. O., Ronquillo-Lomelí, G., Ventura-Ramos, E. J., & Trejo-Perea, M. (2021) · Applied Sciences · doi:10.3390/app11031044 · Cited by 112 · see citing works
Siloed Federated Learning for Multi-centric Histopathology Datasets
Andreux, M., du Terrail, J. O., Béguier, C., & Tramel, E. W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60548-3_13 · Cited by 111 · see citing works
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The application of convolutional neural network to stem cell biology
Kusumoto, D., & Yuasa, S. (2019) · Inflammation and Regeneration · doi:10.1186/s41232-019-0103-3 · Cited by 108 · see citing works
A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology
Anghel, A., Stanisavljević, M., Andani, S., Papandreou, N., Rüschoff, J. H., Wild, P. J., Gabrani, M., & Pozidis, H. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00193 · Cited by 106 · see citing works
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- Authors
- Komura, D., & Ishikawa, S.
- Affiliations
- Tokyo Medical and Dental University (JP)
- Corresponding author
- Komura, Daisuke
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2018-01
- Volume
- 16
- Pages
- 34-42
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC6158771
- OpenAlex
- W2751723768
- Cited by
- 937
- References
- 111
- Citations by year
- 2026: 95 · 2025: 111 · 2024: 104 · 2023: 122 · 2022: 114 · 2021: 140 · 2020: 146 · 2019: 88 · 2018: 17
- FWCI
- 11.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Funders
- JSPS (25710020)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- digital histopathology, histopathology image analysis, computer-aided diagnosis, machine learning
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