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Image analysis and machine learning in digital pathology: Challenges and opportunities
Open accessAbstract
With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data". It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine "sub-visual" image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology.
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“The success of deep learning in computer vision (CV) analysis has significantly advanced its clinical applications in comprehending biomedical images, including CT scans [88], positron emission tomography (PET)-CT scans [89], whole-slide images (WSIs) [90], and MRI scans [91].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [90]
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Self-citationWang, H., Roa, Á. A. C., Basavanhally, A. N., Gilmore, H. L., Shih, N. N. C., Feldman, M., Tomaszewski, J., Gonzalez, F. A., & Madabhushi, A. (2014) · Journal of medical imaging · doi:10.1117/1.jmi.1.3.034003 · Cited by 362
Self-citationA Stochastic Polygons Model for Glandular Structures in Colon Histology Images
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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
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Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698
Self-citationHover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
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Deep learning in histopathology: the path to the clinic
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Metadata
- Authors
- Madabhushi, A., & Lee, G.
- Affiliations
- Case Western Reserve University (US)
- Corresponding author
- Madabhushi, Anant
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 33
- Pages
- 170-175
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC5556681
- OpenAlex
- W2470965540
- Cited by
- 1,093
- References
- 29
- Citations by year
- 2026: 132 · 2025: 140 · 2024: 128 · 2023: 114 · 2022: 122 · 2021: 131 · 2020: 140 · 2019: 94 · 2018: 58 · 2017: 32 · 2016: 1
- FWCI
- 50.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Medical Image Analysis (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute of the National Institutes of Health (1U24CA199374, 1R01CA202752, R21CA179327, R21CA195152, K01ES026841)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK098503)
- DOD Prostate Cancer Synergistic Idea Development Award (PC120857)
- DOD Lung Cancer Idea Development New Investigator Award (LC130463)
- DOD Prostate Cancer Idea Development Award (PCRP W81XWH-15-1-0558)
- Case Comprehensive Cancer Center Pilot Grant
- Cleveland Clinic
- Case Western Reserve University
- 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 pathology, whole slide images, image analysis, machine learning, deep learning, object detection, tissue classification, image segmentation, feature extraction, handcrafted features, multi-omics integration, outcome prediction, precision medicine, telepathology
- MeSH terms
- Machine Learning, Humans, Image Processing, Computer-Assisted, Pathology, Radiology, Telepathology, Precision Medicine
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