dc.contributor.author | Wang, J | |
dc.contributor.author | Zhao, Y | |
dc.contributor.author | Qian, L | |
dc.contributor.author | Yu, X | |
dc.contributor.author | Gao, Y | |
dc.date.accessioned | 2022-03-01T05:41:53Z | |
dc.date.available | 2022-03-01T05:41:53Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 9781665417099 | |
dc.identifier.doi | 10.1109/DICTA52665.2021.9647299 | |
dc.identifier.uri | http://hdl.handle.net/10072/412785 | |
dc.description.abstract | The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e.g., diabetic, hypertensive and solar retinopathies. Existing works often fail in predicting the abnormal areas, e.g, sudden brighter and darker areas and are inclined to predict a pixel to background due to the significant class imbalance, leading to high accuracy and specificity while low sensitivity. To that end, we propose a novel error attention refining network (ERA-Net) that is capable of learning and predicting the potential false predictions in a two-stage manner for effective retinal vessel segmentation. The proposed ERA-Net in the refine stage drives the model to focus on and refine the segmentation errors produced in the initial training stage. To achieve this, unlike most previous attention approaches that run in an unsupervised manner, we introduce a novel error attention mechanism which considers the differences between the ground truth and the initial segmentation masks as the ground truth to supervise the attention map learning. Experimental results demonstrate that our method achieves state-of-the-art performance on two common retinal blood vessel datasets. Code is available at this link. | |
dc.description.peerreviewed | Yes | |
dc.description.sponsorship | Griffith University | |
dc.language | English | |
dc.publisher | IEEE | |
dc.publisher.place | Piscataway, NJ | |
dc.relation.ispartofconferencename | 2021 Digital Image Computing: Techniques and Applications (DICTA) | |
dc.relation.ispartofconferencetitle | DICTA 2021 - 2021 International Conference on Digital Image Computing: Techniques and Applications | |
dc.relation.ispartofdatefrom | 2021-11-29 | |
dc.relation.ispartofdateto | 2021-12-01 | |
dc.relation.ispartoflocation | Gold Coast, Australia | |
dc.subject.fieldofresearch | Biomedical engineering | |
dc.subject.fieldofresearchcode | 4003 | |
dc.title | EAR-NET: Error Attention Refining Network for Retinal Vessel Segmentation | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Wang, J; Zhao, Y; Qian, L; Yu, X; Gao, Y, EAR-NET: Error Attention Refining Network for Retinal Vessel Segmentation, DICTA 2021 - 2021 International Conference on Digital Image Computing: Techniques and Applications, 2021 | |
dc.date.updated | 2022-02-28T23:26:14Z | |
dc.description.version | Accepted Manuscript (AM) | |
gro.rights.copyright | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Gao, Yongsheng | |