| Title | Multi-Task Classification and Segmentation for Explicable Capsule Endoscopy Diagnostics |
| Creator | |
| Date Issued | 2021-08-19 |
| Source Publication | Frontiers in Molecular Biosciences
 |
| Volume | 8 |
| Abstract | Capsule endoscopy is a leading diagnostic tool for small bowel lesions which faces certain challenges such as time-consuming interpretation and harsh optical environment inside the small intestine. Specialists unavoidably waste lots of time on searching for a high clearness degree image for accurate diagnostics. However, current clearness degree classification methods are based on either traditional attributes or an unexplainable deep neural network. In this paper, we propose a multi-task framework, called the multi-task classification and segmentation network (MTCSN), to achieve joint learning of clearness degree (CD) and tissue semantic segmentation (TSS) for the first time. In the MTCSN, the 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 the Capsule-Endoscopy Crohn’s Disease dataset, which introduces the challenges faced in the real world including motion blur, excreta occlusion, reflection, and various complex alimentary scenes that are widely acknowledged in endoscopy examination. Extensive experiments and ablation studies report the significant performance gains of the MTCSN over state-of-the-art methods. |
| Keyword | Auxiliary diagnosis Capsule endoscopy Crohn’s disease Explicable Multi-task learning |
| DOI | 10.3389/fmolb.2021.614277 |
| URL | View source |
| Language | 英语English |
| Scopus ID | 2-s2.0-85114348924 |
| Citation statistics | |
| Document Type | Journal article |
| Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/5973 |
| Collection | Beijing Normal-Hong Kong Baptist University |
| Corresponding Author | Chen，Jie |
| Affiliation | 1.School of Electonic and Computer Engineering,Peking University,Shenzhen,China 2.Department of Gastroenterology,Peking University Shenzhen Hospital,Shenzhen,China 3.Peng Cheng Laboratory,Shenzhen,China 4.Sun Yat-sen University,Guangzhou,China 5.Pennsylvania State University,Philadelphia,United States 6.Harbin Institute of Technology (Shenzhen),Shenzhen,China 7.Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai,China |
| Recommended Citation GB/T 7714 | Kong，Zishang,He，Min,Luo，Qianjianget al. Multi-Task Classification and Segmentation for Explicable Capsule Endoscopy Diagnostics[J]. Frontiers in Molecular Biosciences, 2021, 8. |
| APA | Kong，Zishang., He，Min., Luo，Qianjiang., Huang，Xiansong., Wei，Pengxu., .. & Chen，Jie. (2021). Multi-Task Classification and Segmentation for Explicable Capsule Endoscopy Diagnostics. Frontiers in Molecular Biosciences, 8. |
| MLA | Kong，Zishang,et al."Multi-Task Classification and Segmentation for Explicable Capsule Endoscopy Diagnostics". Frontiers in Molecular Biosciences 8(2021). |
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