Development of a few-shot learning based model for the classification of colorectal submucosal tumors and polyps on endoscopic images
10.3969/j.issn.1005-202X.2024.07.017
- VernacularTitle:基于少样本学习算法的结直肠粘膜下肿瘤和息肉内镜图像分类系统
- Author:
Yahui WU
1
,
2
;
Shiqi ZHU
;
Yudong WU
;
Rufa ZHANG
;
Jinzhou ZHU
Author Information
1. 苏州大学附属第一医院消化内科,江苏苏州 215006
2. 同济大学附属东方医院儿内科,上海 200120
- Keywords:
few-shot learning;
colorectal submucosal tumor;
colorectal polyp;
endoscopic image;
deep learning
- From:
Chinese Journal of Medical Physics
2024;41(7):897-904
- CountryChina
- Language:Chinese
-
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.
