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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,12,31]]},"abstract":"<jats:p>\n                    Artifacts are prevalent in Optical Coherence Tomography Angiography (OCTA) images, which probably interfere doctor\u2019s diagnosis and greatly limit its utility. Therefore, it is desirable to segment artifacts and assess quality when using them for diagnosis. In this article, we propose an end-to-end network (named\n                    <jats:italic toggle=\"yes\">CCM-Net: C<\/jats:italic>\n                    ontrastive and\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    onsistent\n                    <jats:italic toggle=\"yes\">M<\/jats:italic>\n                    ulti-task Network) to jointly address artifact segmentation and quality classification of OCTA images. We first devise multiple Task-Specific Attention Blocks to integrate deep features at different CNN layers for segmenting artifacts and classifying the quality of the input OCTA image. In this way, the weights of different deep features can be automatically learned and are not the same for the two tasks. Moreover, we devise a contrastive loss and a consistency loss to leverage sample relations for further enhancing prediction accuracy. Specifically, given an input OCTA image, we first augment it with a color jitter and select another OCTA image with the same quality classification label. We then design a contrastive loss so that the segmentation results of the input OCTA image are similar to its enhanced OCTA image, while the segmentation results of the two selected OCTA images are not similar. Besides, we devise a consistency loss on the classification results of the three images, because we can find that these images have the same quality classification labels. Experiments on an in-house OCTA dataset (Multi-OCTA) demonstrate that the proposed CCM-Net outperforms state-of-the-art methods.\n                  <\/jats:p>","DOI":"10.1145\/3765745","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T13:50:15Z","timestamp":1759845015000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["<i>CCM-Net: C<\/i>\n                    ontrastive and\n                    <i>C<\/i>\n                    onsistent\n                    <i>M<\/i>\n                    ulti-\n                    <i>T<\/i>\n                    ask Network for Artifact Segmentation and Quality Classification of OCTA Images"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6303-8178","authenticated-orcid":false,"given":"Yang","family":"Wen","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2608-9026","authenticated-orcid":false,"given":"Xiang-Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Shanghai Sixth People\u2019s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3295-4754","authenticated-orcid":false,"given":"Jixue","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1503-0240","authenticated-orcid":false,"given":"Ping","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computing and the School of Design, The Hong Kong Polytechnic University, Kowloon, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3871-663X","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Thrust of Robotics and Autonomous Systems (ROAS), The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China and Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7059-0929","authenticated-orcid":false,"given":"Jing","family":"Qin","sequence":"additional","affiliation":[{"name":"Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Kowloon, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4029-3322","authenticated-orcid":false,"given":"Xiaokang","family":"Yang","sequence":"additional","affiliation":[{"name":"AI Institute, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8678-2784","authenticated-orcid":false,"given":"Bin","family":"Sheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.18502\/jovr.v16i2.9091"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3307724"},{"issue":"6","key":"e_1_3_1_4_2","first-page":"18","article-title":"Imaging artifacts and segmentation errors with wide-field swept-source optical coherence tomography angiography in diabetic retinopathy","volume":"8","author":"Cui Y.","year":"2019","unstructured":"Y. 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