dc.contributor.author | Vats, Anuja | |
dc.contributor.author | Raja, Kiran | |
dc.contributor.author | Pedersen, Marius | |
dc.contributor.author | Mohammed, Ahmed Kedir | |
dc.date.accessioned | 2023-02-28T08:24:21Z | |
dc.date.available | 2023-02-28T08:24:21Z | |
dc.date.created | 2022-10-11T12:51:56Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | IEEE Access. 2022, 10 91414-91423. | en_US |
dc.identifier.issn | 2169-3536 | |
dc.identifier.uri | https://hdl.handle.net/11250/3054499 | |
dc.description.abstract | Early diagnosis of gastrointestinal pathologies leads to timely medical intervention and prevents disease development. Wireless Capsule Endoscopy (WCE) is used as a non-invasive alternative for gastrointestinal examination. WCE can capture images despite the structural complexity presented by human anatomy and can help in detecting pathologies. However, despite recent progress in fine-grained pathology classification and detection, limited works focus on generalization. We propose a multi-channel encoder-decoder network for learning a generalizable fine-grained pathology classifier. Specifically, we propose to use structural residual cues to explicitly impose the network to learn pathology traces. While residuals are extracted using well-established 2D wavelet decomposition, we also propose to use colour channels to learn discriminative cues in WCE images (like red color in bleeding). With less than 40% data (fewer than 2500 labels) used for training, we demonstrate the effectiveness of our approach in classifying different pathologies on two different WCE datasets (different capsule modalities). With a comprehensive benchmark for WCE abnormality and multi-class classification, we illustrate the generalizability of the proposed approach on both datasets, where our results perform better than the state-of-the-art with much fewer labels in abnormality sensitivity on several of nine different pathologies and establish a new benchmark with specificity >97% across classes. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Multichannel Residual Cues for Fine-Grained Classification in Wireless Capsule Endoscopy | en_US |
dc.title.alternative | Multichannel Residual Cues for Fine-Grained Classification in Wireless Capsule Endoscopy | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 91414-91423 | en_US |
dc.source.volume | 10 | en_US |
dc.source.journal | IEEE Access | en_US |
dc.identifier.doi | 10.1109/ACCESS.2022.3201515 | |
dc.identifier.cristin | 2060461 | |
dc.relation.project | Norges forskningsråd: 300031 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |