Development of a few-shot learning based model for the classification of colorectal submucosal tumors and polyps on endoscopic images(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2024年第7期
- Page:
- 897-904
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Development of a few-shot learning based model for the classification of colorectal submucosal tumors and polyps on endoscopic images
- Author(s):
- WU Yahui1; 2; ZHU Shiqi1; WU Yudong3; ZHANG Rufa4; ZHU Jinzhou1
- 1. Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou 215006, China 2. Department of Pediatric Internal Medicine, Shanghai East Hospital, Tongji University, Shanghai 200120, China 3. Department of Internal Medicine, Suzhou Xiangcheng District Yangcheng Lake Peoples Hospital, Suzhou 215138, China 4. Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou 215500, China
- Keywords:
- Keywords: few-shot learning colorectal submucosal tumor colorectal polyp endoscopic image deep learning
- PACS:
- R318；R574
- DOI:
- DOI:10.3969/j.issn.1005-202X.2024.07.017
- Abstract:
- Abstract: Objective To address the difficulty in collecting sufficient endoscopic images of colorectal submucosal tumors for traditional deep learning model training, a few-shot learning based model (FSL model) is proposed for classifying colorectal submucosal tumors and polyps on endoscopic images. Methods A total of 172 endoscopic images of colorectal submucosal tumors were collected from different centers, including 43 each of colorectal lipomas (CRLs), neuroendocrine tumors (NETs), serrated lesions and polyps (SLPs), and traditional adenomas. A support set and a query set were constructed using these endoscopic images. ResNet50 which was pre-trained on ImageNet and esophageal endoscopic images was used to extract image features. Subsequently, K-nearest neighbors algorithm was used for classification based on the calculated Euclidean distance. The classification performance of FSL model was evaluated through the comparison with the original model and endoscopists. Results FSL model had a 4-class classification accuracy of 0.831, Macro AUC of 0.925, Macro F1-score of 0.831 moreover, the proposed model achieved diagnostic accuracies of 0.925 and 0.906 for CRLs and NETs, with F1 score of 0.850 and 0.805. Additionally, the proposed model exhibited high classification consistency (Kappa=0.775) and interpretability. Conclusion The established FSL model performs well in distinguishing CRLs, NETs, SLPs and traditional adenomas on endoscopic images, indicating its potential utility in assisting the identification of colorectal submucosal tumors under endoscopy.
Last Update: 2024-07-13