Multi-Task Classification and Segmentation for Explicable Capsule Endoscopy Diagnostics - Kong, Zishang; He, Min; Luo, Qianjiang; Huang, Xiansong; Wei, Pengxu; Cheng, Yalu; Chen, Luyang; Liang, Yongsheng; Lu, Yanchang; Li, Xi; Chen, Jie (Frontiers in Molecular Biosciences, 2021)
Capsule Endoscopy is a leading diagnostic tool for small bowel lesions which is faced with challenges, like time consuming interpreting and harsh optical environment inside small intestine. Specialists unavoidably wasted lots of time on searching high clearness degree image for accurate diagnostic. However, current clearness degree classification methods are either based on traditional attributes or unexplainable deep neural network. In this paper, we propose a multi-task framework, called Multi-Task Classification and Segmentation Network(MTCSN), to achieve a joint learning of clearness degree(CD) and tissue semantic segmentation(TSS) for the first time. In MTCSN, CD helps to generate better refined TSS, while TSS provides an explicable semantic map to better classify the CD. In addition, we present a new benchmark, named Capsule-Endoscopy Crohn’s Disease dataset, which introduces the challenges faced in real world including motion blur, excreta occlusion, refection and various complex alimentary scenes that are widely existed in endoscopy examination. Extensive experiments and ablation studies report the significant performance gains of MTCSN over state-of-the-art methods.