{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T13:22:55Z","timestamp":1788960175704,"version":"build-2803163510"},"reference-count":56,"publisher":"IOP Publishing","issue":"7","license":[{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62076058"],"award-info":[{"award-number":["62076058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2024,4,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Accurate polyp segmentation from colo-noscopy images plays a crucial role in the early diagnosis and treatment of colorectal cancer. However, existing polyp segmentation methods are inevitably affected by various image noises, such as reflections, motion blur, and feces, which significantly affect the performance and generalization of the model. In addition, coupled with ambiguous boundaries between polyps and surrounding tissue, i.e. small inter-class differences, accurate polyp segmentation remains a challenging problem.\n                    <jats:italic>Approach.<\/jats:italic>\n                    To address these issues, we propose a novel two-stage polyp segmentation method that leverages a preprocessing sub-network (Pre-Net) and a dynamic uncertainty mining network (DUMNet) to improve the accuracy of polyp segmentation. Pre-Net identifies and filters out interference regions before feeding the colonoscopy images to the polyp segmentation network DUMNet. Considering the confusing polyp boundaries, DUMNet employs the uncertainty mining module (UMM) to dynamically focus on foreground, background, and uncertain regions based on different pixel confidences. UMM helps to mine and enhance more detailed context, leading to coarse-to-fine polyp segmentation and precise localization of polyp regions.\n                    <jats:italic>Main results.<\/jats:italic>\n                    We conduct experiments on five popular polyp segmentation benchmarks: ETIS, CVC-ClinicDB, CVC-ColonDB, EndoScene, and Kvasir. Our method achieves state-of-the-art performance. Furthermore, the proposed Pre-Net has strong portability and can improve the accuracy of existing polyp segmentation models.\n                    <jats:italic>Significance.<\/jats:italic>\n                    The proposed method improves polyp segmentation performance by eliminating interference and mining uncertain regions. This aids doctors in making precise and reduces the risk of colorectal cancer. Our code will be released at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/zyh5119232\/DUMNet\" xlink:type=\"simple\">https:\/\/github.com\/zyh5119232\/DUMNet<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad2b94","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T17:27:30Z","timestamp":1708536450000},"page":"075016","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Polyp segmentation with interference filtering and dynamic uncertainty mining"],"prefix":"10.1088","volume":"69","author":[{"given":"Yunhua","family":"Zhang","sequence":"first","affiliation":[{"name":"Northeastern University"},{"name":"DUT Artificial Intelligence Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Yang","sequence":"additional","affiliation":[{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congjin","family":"Gong","sequence":"additional","affiliation":[{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8352-1762","authenticated-orcid":true,"given":"Yutao","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"pmbad2b94bib1","doi-asserted-by":"publisher","first-page":"64","DOI":"10.5009\/gnl.2012.6.1.64","type":"journal-article","article-title":"The miss rate for colorectal adenoma determined by quality-adjusted, back-to-back colonoscopies","volume":"6","author":"Ahn","year":"2012","journal-title":"Gut and liver"},{"key":"pmbad2b94bib2","first-page":"69","type":"conference-proceedings","article-title":"Polyp segmentation in colonoscopy images using fully convolutional network","author":"Akbari","year":"2018"},{"key":"pmbad2b94bib3","doi-asserted-by":"publisher","first-page":"101900","DOI":"10.1016\/j.media.2020.101900","type":"journal-article","article-title":"A deep learning framework for quality assessment and restoration in video endoscopy","volume":"68","author":"Ali","year":"2021a","journal-title":"Med. Image Anal."},{"key":"pmbad2b94bib4","doi-asserted-by":"publisher","first-page":"102002","DOI":"10.1016\/j.media.2021.102002","type":"journal-article","article-title":"Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy,","volume":"70","author":"Ali","year":"2021b","journal-title":"Med. Image Anal."},{"key":"pmbad2b94bib5","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","type":"journal-article","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation versus saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"pmbad2b94bib6","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1109\/TMI.2017.2664042","type":"journal-article","article-title":"Comparative validation of polyp detection methods in video colonoscopy: results from the miccai 2015 endoscopic vision challenge,","volume":"36","author":"Bernal","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbad2b94bib7","article-title":"Weight uncertainty in neural network,","author":"Blundell","year":"2015","type":"other"},{"key":"pmbad2b94bib8","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","type":"journal-article","article-title":"Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"pmbad2b94bib9","first-page":"234","type":"conference-proceedings","article-title":"Reverse attention for salient object detection","author":"Chen","year":"2018"},{"key":"pmbad2b94bib10","doi-asserted-by":"publisher","first-page":"102994","DOI":"10.1016\/j.media.2023.102994","type":"journal-article","article-title":"A temporal learning approach to inpainting endoscopic specularities and its effect on image correspondence","volume":"90","author":"Daher","year":"2023","journal-title":"Medical Image Analysis"},{"key":"pmbad2b94bib11","doi-asserted-by":"publisher","first-page":"101530","DOI":"10.1016\/j.bspc.2019.04.007","type":"journal-article","article-title":"Computer-aided polyp detection based on image enhancement and saliency-based selection","volume":"55","author":"Deeba","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"pmbad2b94bib12","doi-asserted-by":"publisher","first-page":"9150015","DOI":"10.26599\/AIR.2023.9150015","type":"journal-article","article-title":"Polyp-PVT: Polyp segmentation with pyramid vision transformers","volume":"2","author":"Dong","year":"2023","journal-title":"CAAI Artificial Intelligence Research"},{"key":"pmbad2b94bib13","first-page":"263","type":"conference-proceedings","article-title":"Pranet: parallel reverse attention network for polyp segmentation","author":"Fan","year":"2020"},{"key":"pmbad2b94bib14","first-page":"302","type":"conference-proceedings","article-title":"Selective feature aggregation network with area-boundary constraints for polyp segmentation","author":"Fang","year":"2019"},{"key":"pmbad2b94bib15","first-page":"137","type":"journal-article","article-title":"Deeplabv3+ _encoder\u2013decoder with atrous separable convolution for semantic image segmentation, Pertanika","volume":"34","author":"Firdaus-Nawi","year":"2011","journal-title":"J. Trop. Agric. Sci"},{"key":"pmbad2b94bib16","first-page":"8","type":"book","article-title":"Generative adversarial networks for specular highlight removal in endoscopic images","volume":"vol 10576","author":"Funke","year":"2018"},{"key":"pmbad2b94bib17","first-page":"1050","type":"journal-article","article-title":"Dropout as a bayesian approximation: representing model uncertainty in deep learning","volume":"PMLR","author":"Gal","year":"2016","journal-title":"international conference on machine learning"},{"key":"pmbad2b94bib18","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","type":"journal-article","article-title":"Res2net: a new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"pmbad2b94bib19","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","type":"journal-article","article-title":"Region-based convolutional networks for accurate object detection and segmentation","volume":"38","author":"Girshick","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"pmbad2b94bib20","first-page":"7132","type":"conference-proceedings","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"pmbad2b94bib21","first-page":"451","type":"conference-proceedings","article-title":"Kvasir-seg: a segmented polyp dataset","author":"Jha","year":"2020"},{"key":"pmbad2b94bib22","doi-asserted-by":"publisher","first-page":"2167","DOI":"10.1145\/3474085.3475375","type":"conference-proceedings","article-title":"Uacanet: uncertainty augmented context attention for polyp segmentation","author":"Kim","year":"2021"},{"key":"pmbad2b94bib23","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","type":"journal-article","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"pmbad2b94bib24","type":"conference-proceedings","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"30","author":"Lakshminarayanan","year":"2017"},{"key":"pmbad2b94bib25","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1055\/s-0031-1291666","type":"journal-article","article-title":"Factors influencing the miss rate of polyps in a back-to-back colonoscopy study","volume":"44","author":"Leufkens","year":"2012","journal-title":"Endoscopy"},{"key":"pmbad2b94bib26","doi-asserted-by":"publisher","first-page":"135003","DOI":"10.1088\/1361-6560\/acda0d","type":"journal-article","article-title":"Mce-net: polyp segmentation with multiple branch series-parallel attention and channel interaction via edge distribution guidance","volume":"68","author":"Li","year":"2023","journal-title":"Phys. Med. Biol."},{"key":"pmbad2b94bib27","doi-asserted-by":"publisher","first-page":"175041","DOI":"10.1088\/1361-6560\/acf090","type":"journal-article","article-title":"Mcsf-net: a multi-scale channel spatial fusion network for real-time polyp segmentation","volume":"68","author":"Liu","year":"2023","journal-title":"Phys. Med. Biol."},{"key":"pmbad2b94bib28","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2017","type":"conference-proceedings"},{"key":"pmbad2b94bib29","doi-asserted-by":"publisher","first-page":"102517","DOI":"10.1016\/j.media.2022.102517","type":"journal-article","article-title":"Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency","volume":"80","author":"Luo","year":"2022","journal-title":"Med. Image Anal."},{"key":"pmbad2b94bib30","first-page":"13153","type":"journal-article","article-title":"A simple baseline for bayesian uncertainty in deep learning","volume":"32","author":"Maddox","year":"2019","journal-title":"Neural Inf. Process. Syst."},{"key":"pmbad2b94bib31","first-page":"1","type":"conference-proceedings","article-title":"Superpixel based segmentation and classification of polyps in wireless capsule endoscopy","author":"Maghsoudi","year":"2017"},{"key":"pmbad2b94bib32","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TMI.2014.2314959","type":"journal-article","article-title":"Automated polyp detection in colon capsule endoscopy","volume":"33","author":"Mamonov","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbad2b94bib33","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1136\/gutjnl-2022-327736","type":"journal-article","article-title":"Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from globocan","volume":"72","author":"Morgan","year":"2023","journal-title":"Gut"},{"key":"pmbad2b94bib34","first-page":"15","type":"conference-proceedings","article-title":"Neounet: towards accurate colon polyp segmentation and neoplasm detection","author":"Ngoc Lan","year":"2021"},{"key":"pmbad2b94bib35","first-page":"633","type":"conference-proceedings","article-title":"Ccbanet: cascading context and balancing attention for polyp segmentation","author":"Nguyen","year":"2021"},{"key":"pmbad2b94bib36","first-page":"792","type":"conference-proceedings","article-title":"Bdg-net: boundary distribution guided network for accurate polyp segmentation","volume":"vol 12032","author":"Qiu","year":"2022"},{"key":"pmbad2b94bib37","first-page":"234","type":"conference-proceedings","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"pmbad2b94bib38","first-page":"4510","type":"conference-proceedings","article-title":"Mobilenetv2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"key":"pmbad2b94bib39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3082315","type":"journal-article","article-title":"Detection of polyps in colonoscopic videos using saliency map-based modified particle filter","volume":"70","author":"Sasmal","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"pmbad2b94bib40","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","type":"journal-article","article-title":"Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer","volume":"9","author":"Silva","year":"2014","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"pmbad2b94bib41","first-page":"851","type":"other","article-title":"Colorectal polyp segmentation by u-net with dilation convolution","author":"Sun","year":"2019"},{"key":"pmbad2b94bib42","doi-asserted-by":"publisher","first-page":"209\u2013","DOI":"10.3322\/caac.21660","type":"journal-article","article-title":"Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA: Cancer J. Clin."},{"key":"pmbad2b94bib43","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TMI.2015.2487997","type":"journal-article","article-title":"Automated polyp detection in colonoscopy videos using shape and context information","volume":"35","author":"Tajbakhsh","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbad2b94bib44","first-page":"307","type":"conference-proceedings","article-title":"Ddanet: dual decoder attention network for automatic polyp segmentation","author":"Tomar","year":"2021"},{"key":"pmbad2b94bib45","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/s11042-020-08849-y","type":"journal-article","article-title":"A brief survey of visual saliency detection","volume":"79","author":"Ullah","year":"2020","journal-title":"Multimedia Tools Appl."},{"key":"pmbad2b94bib46","doi-asserted-by":"publisher","first-page":"4037190","DOI":"10.1155\/2017\/4037190","type":"journal-article","article-title":"A benchmark for endoluminal scene segmentation of colonoscopy images","volume":"2017","author":"V\u00e1zquez","year":"2017","journal-title":"J. Healthcare Eng."},{"key":"pmbad2b94bib47","doi-asserted-by":"publisher","first-page":"102447","DOI":"10.1016\/j.media.2022.102447","type":"journal-article","article-title":"Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning","volume":"79","author":"Wang","year":"2022","journal-title":"Med. Image Anal."},{"key":"pmbad2b94bib48","first-page":"67","type":"conference-proceedings","article-title":"Boxpolyp: boost generalized polyp segmentation using extra coarse bounding box annotations","author":"Wei","year":"2022"},{"key":"pmbad2b94bib49","first-page":"699","type":"conference-proceedings","article-title":"Shallow attention network for polyp segmentation","author":"Wei","year":"2021"},{"key":"pmbad2b94bib50","first-page":"321","type":"conference-proceedings","article-title":"F3net: fusion, feedback and focus for salient object detection","volume":"vol 34","author":"Wei","year":"2020"},{"key":"pmbad2b94bib51","first-page":"1395","type":"other","article-title":"Holistically-nested edge detection","author":"Xie","year":"2015"},{"key":"pmbad2b94bib52","first-page":"99","type":"conference-proceedings","article-title":"Lesion-aware dynamic kernel for polyp segmentation","author":"Zhang","year":"2022"},{"key":"pmbad2b94bib53","first-page":"253","type":"conference-proceedings","article-title":"Adaptive context selection for polyp segmentation","author":"Zhang","year":"2020"},{"key":"pmbad2b94bib54","first-page":"120","type":"conference-proceedings","article-title":"Automatic polyp segmentation via multi-scale subtraction network","author":"Zhao","year":"2021"},{"key":"pmbad2b94bib55","doi-asserted-by":"publisher","first-page":"109555","DOI":"10.1016\/j.patcog.2023.109555","type":"journal-article","article-title":"Cross-level feature aggregation network for polyp segmentation","volume":"140","author":"Zhou","year":"2023","journal-title":"Pattern Recognit."},{"key":"pmbad2b94bib56","first-page":"3","type":"conference-proceedings","article-title":"Unet++: a nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2b94","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2b94\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2b94","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2b94\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2b94\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2b94\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2b94\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2b94\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T16:23:57Z","timestamp":1778775837000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2b94"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,26]]},"references-count":56,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,3,26]]},"published-print":{"date-parts":[[2024,4,7]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/ad2b94","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,26]]},"assertion":[{"value":"Polyp segmentation with interference filtering and dynamic uncertainty mining","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2023-09-12","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-02-21","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-03-26","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}