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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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![Fig. 1 Typical steps for machine learning in digital pathological image analysis. After preprocessing whole slide images, various types of machine learning algorithms could be applied including (a) supervised learning (see Section 2), (b) unsupervised learning (see Section 2), (c) semi-supervised learning (see Section 4.2.2), and (d) multiple instance learning (see Section 4.2.2). The histopathological images are adopted from The Cancer Genome Atlas (TCGA) [33].](/paper-figures/b0261b5ffb1543c2859222923155fa5f/machine-learning-methods-for-histopathological-image-analysis-figure-1.jpg)
![Fig. 2 Multiple magnification levels of the same histopathological image. Right images show the magnified region indicated by red box on the left images. Leftmost image clearly shows papillary structure, and rightmost image clearly shows nucleus of each cell. The histopathological images are adopted from TCGA [33].](/paper-figures/39ce5bc3f03048e88a1d0910f599effc/machine-learning-methods-for-histopathological-image-analysis-figure-2.jpg)
![Fig. 3 Artifacts in WSIs. Top: tumor region is outlined with red marker. The arrow indicates a tear possibly formed during the tissue preparation process. Left bottom: blurred image. Right bottom: folded tissue section. The histopathological images are adopted from TCGA [33].](/paper-figures/2d261e5c8f574ef28f148c08ebb8cce0/machine-learning-methods-for-histopathological-image-analysis-figure-3.jpg)
![Fig. 4 Color variation of histopathological images. Both of these two images show lymphocytes. The histopathological images are adopted from TCGA [33].](/paper-figures/820784a850cf4fb488451917a7d41b02/machine-learning-methods-for-histopathological-image-analysis-figure-4.jpg)
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“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
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“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
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Cites
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Causability and explainability of artificial intelligence in medicine
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885
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
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232
Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging
Najjar, R. (2023) · Diagnostics · doi:10.3390/diagnostics13172760 · Cited by 1,025
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Metadata
- 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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