{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T01:00:58Z","timestamp":1790298058932,"version":"4.1.0"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Mol. Biosci."],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3389\/fmolb.2021.614277","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T14:28:51Z","timestamp":1629383331000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Multi-Task Classification and Segmentation for Explicable Capsule Endoscopy Diagnostics"],"prefix":"10.3389","volume":"8","author":[{"given":"Zishang","family":"Kong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianjiang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiansong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengxu","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yalu","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luyang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongsheng","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanchang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"104037","DOI":"10.1016\/j.compbiomed.2020.104037","article-title":"Multi-Task Deep Learning Based CT Imaging Analysis for COVID-19 Pneumonia: Classification and Segmentation","volume":"126","author":"Amyar","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1016\/s0140-6736(12)60026-9","article-title":"Crohn's Disease","volume":"380","author":"Baumgart","year":"2012","journal-title":"The Lancet."},{"key":"B3","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.gie.2008.04.016","article-title":"A Validation Study of 3 Grading Systems to Evaluate Small-Bowel Cleansing for Wireless Capsule Endoscopy: a Quantitative Index, a Qualitative Evaluation, and an Overall Adequacy Assessment","volume":"69","author":"Brotz","year":"2009","journal-title":"Gastrointest. Endosc."},{"key":"B4","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/a:1007379606734","article-title":"Multitask Learning","volume":"28","author":"Caruana","year":"1997","journal-title":"Machine Learn."},{"key":"B5","first-page":"801","article-title":"Encoder-Decoder With Atrous Separable Convolution for Semantic Image Segmentation","author":"Chen","year":"2018"},{"key":"B6","first-page":"1349","article-title":"Fine-Grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach","author":"Gebru","year":"2017"},{"key":"B7","first-page":"770","article-title":"Deep Residual Learning for Image Recognition","author":"He","year":"2016"},{"key":"B8","first-page":"4700","article-title":"Densely Connected Convolutional Networks","author":"Huang","year":"2017"},{"key":"B9","article-title":"An Improved Classification and Localization Approach to Small Bowel Capsule Endoscopy Using Convolutional Neural Network","author":"Hwang","year":"2020","journal-title":"Dig. Endosc."},{"key":"B10","first-page":"964","article-title":"A Dirty Model for Multi-Task Learning","author":"Jalali","year":"2010"},{"key":"B11","first-page":"7482","article-title":"Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics","author":"Kendall","year":"2018"},{"key":"B12","first-page":"1097","article-title":"Imagenet Classification With Deep Convolutional Neural Networks","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"B13","article-title":"Multitask Classification and Segmentation for Cancer Diagnosis in Mammography","author":"Le","year":"2019"},{"key":"B14","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/tmi.2018.2845918","article-title":"H-Denseunet: Hybrid Densely Connected Unet for Liver and Tumor Segmentation from Ct Volumes","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans. Med. Imaging."},{"key":"B15","first-page":"1871","article-title":"End-to-end Multi-Task Learning with Attention","author":"Liu","year":"2019"},{"key":"B16","first-page":"3431","article-title":"Fully Convolutional Networks for Semantic Segmentation","author":"Long","year":"2015"},{"key":"B17","doi-asserted-by":"publisher","first-page":"388","DOI":"10.5009\/gnl18384","article-title":"Overview of Deep Learning in Gastrointestinal Endoscopy","volume":"13","author":"Min","year":"2019","journal-title":"Gut and liver."},{"key":"B18","first-page":"3994","article-title":"Cross-Stitch Networks for Multi-Task Learning","author":"Misra","year":"2016"},{"key":"B19","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1056\/nejm199109263251306","article-title":"Inflammatory Bowel Disease","volume":"325","author":"Podolsky","year":"1991","journal-title":"N. Engl. J. Med."},{"key":"B20","first-page":"234","article-title":"U-net: Convolutional Networks for Biomedical Image Segmentation","author":"Ronneberger","year":"2015"},{"key":"B21","article-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","author":"Simonyan","year":"2014"},{"key":"B22","first-page":"1375","article-title":"Many Task Learning With Task Routing","author":"Strezoski","year":"2019"},{"key":"B23","article-title":"A Novel Multi-Task Deep Learning Model for Skin Lesion Segmentation and Classification","author":"Yang","year":"2017"},{"key":"B24","first-page":"2881","article-title":"Pyramid Scene Parsing Network","author":"Zhao","year":"2017"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging."}],"container-title":["Frontiers in Molecular Biosciences"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmolb.2021.614277\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T23:36:08Z","timestamp":1629416168000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmolb.2021.614277\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,19]]},"references-count":25,"alternative-id":["10.3389\/fmolb.2021.614277"],"URL":"https:\/\/doi.org\/10.3389\/fmolb.2021.614277","relation":{},"ISSN":["2296-889X"],"issn-type":[{"value":"2296-889X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,19]]},"article-number":"614277"}}