CLELNet: A continual learning network for esophageal lesion analysis on endoscopic images. (April 2023)

• Record Type: Journal Article
• Authors: Tang, Suigu; Yu, Xiaoyuan; Cheang, Chak Fong; Ji, Xiaoyu; Yu, Hon Ho; Choi, I Cheong
• Is Part Of: Computer methods and programs in biomedicine. Volume 231(2023)
• Publication Date: 2023-04
• Subjects: Continual learning -- Convolutional autoencoder -- Classification -- Esophageal endoscopic images -- Segmentation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107399
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The proposed CLELNet made up of shared layers and task-specific layers were able to continuously learn new tasks for esophageal lesions analysis. To overcome the catastrophic forgetting of The CLELNet, the convolutional autoencoder was used to extract the representation features of esophageal lesions. we brought about the first two tasks: task 1 (classification) was to make the multiclassifications of esophageal lesions, and then task 2 (segmentation) was to perform the segmentation of esophageal lesions. Abstract: Background and Objective: A deep learning-based intelligent diagnosis system can significantly reduce the burden of endoscopists in the daily analysis of esophageal lesions. Considering the need to add new tasks in the diagnosis system, a deep learning model that can train a series of tasks incrementally using endoscopic images is essential for identifying the types and regions of esophageal lesions. Method: In this paper, we proposed a continual learning-based esophageal lesion network (CLELNet), in which a convolutional autoencoder was designed to extract representation features of endoscopic images among different esophageal lesions. The proposed CLELNet consists of shared layers and task-specific layers. Shared layers are used to extract common features among different lesions while task-specific layers can complete different tasks. The first two tasks trained by the CLELNet are the classification (task 1) and the segmentation (task 2). We collected a dataset of esophageal endoscopic images from Macau Kiang Wu Hospital for training and testing the CLELNet. Results: The experimental results showed that the classification accuracy of task 1 was 95.96%, and the Intersection Over Union and the Dice Similarity Coefficient of task 2 were 65.66% and 78.08%, respectively. Conclusions: The proposed CLELNet can realize task-incremental learning without forgetting the previous tasks and thus become a useful computer-aided diagnosis system in esophageal lesions analysis.