The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Deep Learning and Its Applications in Computational Pathology
Open accessPreprint — published as 10.3390/biomedinformatics2010010 · Paper page
Abstract
Deep learning techniques, such as convolutional neural networks (CNN), generative adversarial networks (GAN), and graph neural networks (GNN), have over the past decade changed the ac-curacy of prediction in many diverse fields. In recent years, the application of deep learning tech-niques in computer vision tasks in pathology demonstrated extraordinary potential in assisting clinicians, automating diagnosis, and reducing costs for patients. Formerly unknown pathologi-cal evidence, such as morphological features related to specific biomarkers, copy number varia-tions, and other molecular features, were also able to be captured by deep learning models. In this paper, we review popular deep learning methods and some recent publications about their appli-cations in pathology.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Flagged first, then by citations
Metadata
- Authors
- Hong, R., & Fenyö, D.
- Affiliations
- New York University (US)
- Corresponding author
- Hong, Runyu
- Fenyö, David
- Venue
- BioMedInformatics
- Publisher
- MDPI AG
- Published
- 2022-01-17
- Volume
- 2
- Issue
- 1
- Pages
- 159-168
- ISSN
- 2673-7426 (ISSN-L)
- OpenAlex
- W4225488857
- Cited by
- 13
- References
- 1
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 2 · 2023: 5 · 2022: 1
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BioMedInformatics (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- Funders
- National Institutes of Health
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
