A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.
Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-su...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2342 - 2354 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515421&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515421 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515421 181515421 181515421 10.1007/s10278-024-01123-9 181515421 ppf: 2342 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification. aug: au: Wang, Yu Ni, Haoxiang Zhou, Jielu Liu, Lihe Lin, Jiaxi Yin, Minyue Gao, Jingwen Zhu, Shiqi Yin, Qi Zhu, Jinzhou Li, Rui affil: https://ror.org/028pgd321 Department of Hepatobiliary Surgery, Jintan Affiliated Hospital of Jiangsu University, 213200, Changzhou, Jiangsu, China sug: subj: Colorectal Neoplasms Classification National Institute for Health and Care Excellence Classification Deep Learning Methods Classification Algorithms Human Machine Learning Algorithms Colorectal Neoplasms Diagnosis Deep Learning Evaluation Sensitivity and Specificity Diagnostic Imaging Digital Imaging Diagnosis, Computer Assisted Image Enhancement Colorectal Neoplasms Pathology Colonoscopy Funding Source ab: Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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