Deep Learning and Its Applications in Computational Pathology
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Abstract
Deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and graph neural networks (GNNs) have, over the past decade, changed the accuracy of prediction in many diverse fields. In recent years, the application of deep learning techniques in computer vision tasks in pathology has demonstrated extraordinary potential in assisting clinicians, automating diagnoses, and reducing costs for patients. Formerly unknown pathological evidence, such as morphological features related to specific biomarkers, copy number variations, and other molecular features, could also be captured by deep learning models. In this paper, we review popular deep learning methods and some recent publications about their applications in pathology.
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- Authors
- Hong, R., & Fenyö, D.
- Affiliations
- New York University (US)
- Corresponding author
- Hong, Runyu
- Fenyö, David
- Venue
- BioMedInformatics
- Publisher
- MDPI AG
- Published
- 2022-02-03
- Volume
- 2
- Issue
- 1
- Pages
- 159-168
- ISSN
- 2673-7426 (ISSN-L)
- OpenAlex
- W4225488857
- Cited by
- 14
- References
- 9
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 2 · 2023: 5 · 2022: 2
- FWCI
- 0.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BioMedInformatics (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- Funders
- National Cancer Institute (U24CA210972)
- 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
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- deep learning, computational pathology, convolutional neural networks, generative adversarial networks, graph neural networks, automated diagnosis, biomarker detection, copy number variation, computer-aided diagnosis, histopathology image analysis
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