ArticleNature communications2021
Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.
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Who cites it
63 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of Machine Learning in Diagnosing KRAS (Kirsten Rat Sarcoma) Mutations in Colorectal Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- A Hybrid Lightweight Deep Learning Framework for Colorectal Cancer Detection Using an Adaptive Gated Feature Fusion and Grid Search-Based Hyperparameter Optimization.Journal of imaging informatics in medicine · 2026Article
- Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.BMC cancer · 2026Article
- Uncovering Hidden Prognostic Patterns in Colorectal Cancer Histology Using Unsupervised Learning: A Computational Pathology Study.Bioengineering (Basel, Switzerland) · 2026Article
- A comprehensive European Colorectal Cancer Cohort dataset.Scientific data · 2026Article
- Large-Scale Histological Image Dataset with Metadata for Colorectal Cancer Microenvironment.Scientific data · 2026Article
- Diagnostic accuracy of deep learning models for colorectal cancer detection in histopathological whole-slide images: a systematic review and diagnostic test accuracy meta-analysis.Frontiers in oncology · 2026Review
- Deep learning-based histomorphological subtyping and risk stratification of small cell lung cancer from hematoxylin and eosin-stained whole slide images.Genome medicine · 2025Article
- Accurate colorectal cancer detection using a random hinge exponential distribution coupled attention network on pathological images.Abdominal radiology (New York) · 2025Article
- Class-aware multi-level attention learning for semi-supervised breast cancer diagnosis under imbalanced label distribution.Medical & biological engineering & computing · 2025Article
- Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization.World journal of gastrointestinal oncology · 2025Article
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- Automated Detection of Kaposi Sarcoma-Associated Herpesvirus-Infected Cells in Immunohistochemical Images of Skin Biopsies.JCO global oncology · 2025Article
- Auxiliary meta-learning strategy for cancer recognition: leveraging external data and optimized feature mapping.BMC cancer · 2025Article
- Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis.JHEP reports : innovation in hepatology · 2025Article
- Article
- Embracing change, moving with time: exploring the role of digital technologies and accelerators in promoting community oral health in Africa.Frontiers in oral health · 2025Article
- CsDETC: detection and counting of small targetFrontiers in cellular and infection microbiology · 2025Article
- ViT-Based Face Diagnosis Images Analysis for Schizophrenia Detection.Brain sciences · 2024Article
- Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor.Cell reports. Medicine · 2024Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.