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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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“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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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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