Explainable Multitask Shapley Explanation Networks for Real-Time Polyp Diagnosis in Videos

Colorectal cancer is mostly caused by colorectal polyps, which can be prevented through polyp diagnosis using colonoscopy. The current computer-aided decision-making methods suffer from a variety of drawbacks, including inaccurate polyp classification, poor real-time performance, and poor interpretability. To address these issues, we propose an explainable multitask Shapley explanation networks (EMSEN) that can perform real-time explainable multitasks such as polyp detection and classification in colonoscopy videos. The EMSEN accepts two multimodal inputs of different light sources, and outputs the polyp location, classification type, and diagnosis results according to the real-time colonoscopy video, where efficient channel attention (ECA) Mechanism-based network and Shapley explanation networks (ShapNet) are designed to improve the feature extraction performance and model interpretability, respectively. Extensive experiment studies are conducted to verify the efficiency and effectiveness of the proposed method by comparing with the experts and state-of-the-art methods. The results demonstrate that the developed method performs the best, which achieves competitive diagnosis performance.