Simultaneous segmentation and classification of colon cancer polyp images using a dual branch multi-task learning network

• Published in: Mathematical biosciences and engineering : MBE Vol. 21; no. 2; pp. 2024 - 2049
• Main Authors: Li, Chenqian, Liu, Jun, Tang, Jinshan
• Format: Journal Article
• Language: English
• Published: United States AIMS Press 2024
• Subjects: attention mechanism; Colonic Neoplasms - diagnostic imaging; colonoscopy; dual branch network; Electric Power Supplies; fim; Humans; Image Processing, Computer-Assisted; Learning; multi-task network; Neural Networks, Computer; polyps; rabe; Semantics; polyps; RABE; attention mechanism; dual branch network; FIM; multi-task network; colonoscopy
• ISSN: 1551-0018; 1551-0018
• DOI: 10.3934/mbe.2024090

Accurate classification and segmentation of polyps are two important tasks in the diagnosis and treatment of colorectal cancers. Existing models perform segmentation and classification separately and do not fully make use of the correlation between the two tasks. Furthermore, polyps exhibit random regions and varying shapes and sizes, and they often share similar boundaries and backgrounds. However, existing models fail to consider these factors and thus are not robust because of their inherent limitations. To address these issues, we developed a multi-task network that performs both segmentation and classification simultaneously and can cope with the aforementioned factors effectively. Our proposed network possesses a dual-branch structure, comprising a transformer branch and a convolutional neural network (CNN) branch. This approach enhances local details within the global representation, improving both local feature awareness and global contextual understanding, thus contributing to the improved preservation of polyp-related information. Additionally, we have designed a feature interaction module (FIM) aimed at bridging the semantic gap between the two branches and facilitating the integration of diverse semantic information from both branches. This integration enables the full capture of global context information and local details related to polyps. To prevent the loss of edge detail information crucial for polyp identification, we have introduced a reverse attention boundary enhancement (RABE) module to gradually enhance edge structures and detailed information within polyp regions. Finally, we conducted extensive experiments on five publicly available datasets to evaluate the performance of our method in both polyp segmentation and classification tasks. The experimental results confirm that our proposed method outperforms other state-of-the-art methods.