The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
Open accessPublished version of a preprint
Abstract
Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole slide images of colorectal cancer from 8803 subjects from 13 independent centers. SSL (~3150 labeled, ~40,950 unlabeled; ~6300 labeled, ~37,800 unlabeled patches) performs significantly better than the SL. No significant difference is found between SSL (~6300 labeled, ~37,800 unlabeled) and SL (~44,100 labeled) at patch-level diagnoses (area under the curve (AUC): 0.980 ± 0.014 vs. 0.987 ± 0.008, P value = 0.134) and patient-level diagnoses (AUC: 0.974 ± 0.013 vs. 0.980 ± 0.010, P value = 0.117), which is close to human pathologists (average AUC: 0.969). The evaluation on 15,000 lung and 294,912 lymph node images also confirm SSL can achieve similar performance as that of SL with massive annotations. SSL dramatically reduces the annotations, which has great potential to effectively build expert-level pathological artificial intelligence platforms in practice.
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“Histopathologic classification studies have been reported in colorectal cancer (70,71).”
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Metadata
- Authors
- Yu, G., Sun, K., Xu, C., Shi, X.-H., Wu, C., Xie, T., Meng, R.-Q., Meng, X.-H., Wang, K.-S., Xiao, H.-M., & Deng, H.-W.
- Affiliations
- Central South University (CN)
- University of Oklahoma Health Sciences Center (US)
- Florida State University (US)
- Tulane University (US)
- Corresponding author
- Wang, Kuan-Song
- Xiao, Hong-Mei
- Deng, Hong-Wen
- ORCID
- Venue
- Nature Communications
- Published
- 2021-11-02
- Volume
- 12
- Issue
- 1
- Pages
- 6311-6311
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8563931
- OpenAlex
- W3208123223
- Cited by
- 147
- References
- 60
- Citations by year
- 2026: 22 · 2025: 35 · 2024: 41 · 2023: 33 · 2022: 15 · 2021: 1
- FWCI
- 7.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (#81471453, R01MH104680, R01MH107354, R01GM109068, 81972490)
- Science and Technology Program of Hunan Province (2020YJ004)
- National Institutes of Health
- National Institute on Aging (U19AG055373)
- National Institute of Mental Health (R01MH107354, R01MH104680)
- National Institute of General Medical Sciences (R01GM109068, P20GM109036)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK115679)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR059781, R01AR069055)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- colorectal cancer, semi-supervised learning, whole slide images, pathological image classification, lung cancer, digital pathology, artificial intelligence in pathology
- MeSH terms
- Supervised Machine Learning, Deep Learning, Artificial Intelligence, Humans, Lung Neoplasms, Lymphatic Metastasis, ROC Curve, Colorectal Neoplasms, Neural Networks, Computer
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