Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
Year: 2020
Type: preprint
Abstract: Background: The machine-assisted recognition of colorectal cancer has been mainly focused on supervised deep learning that suffer from a significant bottleneck of requiring massive labeled data. We hypothesize that semi-supervised deep learning leveraging a small number of labeled data can provide a powerful alternative strategy.Method: We proposed a semi-supervised model based on mean teacher that provide pathological predictions at both patch-level and patient-level. We demonstrated the general utility of the model utilizing 13,111 whole slide images from 8,803 subjects gathered from 13 centers. We compared our proposed method with the prevailing supervised learning and six pathologists.Results: with a small amount of labeled training patches (~3,150 labeled, ~40,950 unlabeled or ~6,300 labeled,~37,800 unlabeled), the semi-supervised model performed significantly better than the supervised model (AUC: 0.90 ± 0.06 vs. 0.84 ± 0.07, P value = 0.02 or AUC: 0.98 ± 0.01 vs 0.92 ± 0.04, P value = 0.0004). Moreover, we found no significant difference between the supervised model using massive ~44,100 labeled patches and the semi-supervised model (~6,300 labeled, ~37,800 unlabeled) at patch-level diagnoses (AUC: 0.98 ± 0.01 vs 0.987 ± 0.01, P value = 0.134) and patient-level diagnoses (average AUC: 97.40% vs. 97.96%, P value = 0.117) . Our model was close to human pathologists (average AUC: 97.17% vs. 96.91%).Conclusions: We reported that semi-supervised learning can achieve excellent performance through a multi-center study. We thus suggested that semi-supervised learning has great potentials to build artificial intelligence (AI) platforms, which will dramatically reduce the cost of labeled data and greatly facilitate the development and application of AI in medical sciences.
Authors: Gang Yu, Ting Xie, Chao Xu, Xing-Hua Shi, Chong Wu, Run-Qi Meng, Xiang-He Meng, Kuan-Song Wang, Hong-Mei Xiao, Hong-Wen Deng
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Tulane University, National Natural Science Foundation of China, National Institutes of Health, National Institute on Aging, National Institute of Mental Health, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Arthritis and Musculoskeletal and Skin Diseases
Awards: G1033827860, G131902472, G19956286, Integration of fMRI imaging, genomics, network and biological knowledge, Integration of brain imaging with genomic and epigenomic data, G4044999748, G4160790422, Epigenomewide DNA Methylation Study for Osteoporosis Risk, Decoding Methylation Mediated Epigenomic Contributions to Male Osteoporosis, Integration of multiscale genomic data for comprehensive analysis of complex dise, G5553974194, G5600516950, G571018580, G6623562274, G6943722246, G7643228873, Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases, Nutrigenetics and Nutrigenomics for Precision Weight-Loss Diet Interventions, Trans-omics Integration of Multi-omics Studies for Osteoporosis, G9366763
OpenAlex ID: W4253665432