{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T15:36:05Z","timestamp":1764776165413,"version":"3.46.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Suzhou Science and Technology Planning Project","award":["No. SKJY2021044"],"award-info":[{"award-number":["No. SKJY2021044"]}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["No. BK20130324"],"award-info":[{"award-number":["No. BK20130324"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013286","name":"Specialized Research Fund for the Doctoral Program of Higher Education","doi-asserted-by":"crossref","award":["No. 20123201120009"],"award-info":[{"award-number":["No. 20123201120009"]}],"id":[{"id":"10.13039\/501100013286","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["No. 12KJB510029"],"award-info":[{"award-number":["No. 12KJB510029"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11517-025-03426-7","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T02:39:14Z","timestamp":1755052754000},"page":"3761-3775","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hard exudates segmentation for retinal fundus images based on longitudinal multi-scale fusion network"],"prefix":"10.1007","volume":"63","author":[{"given":"Shuang","family":"Liu","sequence":"first","affiliation":[]},{"given":"Xiangyu","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Zou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"3426_CR1","doi-asserted-by":"crossref","unstructured":"Reichel E, Salz D (2015) Diabetic retinopathy screening. Manag Diabet Eye Dis Clin Pract 25\u201338","DOI":"10.1007\/978-3-319-08329-2_3"},{"issue":"16012","key":"3426_CR2","first-page":"1","volume":"2","author":"TY Wong","year":"2016","unstructured":"Wong TY, Cheung CMG, Larsen M et al (2016) Diabetic retinopathy. Nat Rev Dis Primer 2(16012):1\u201310","journal-title":"Nat Rev Dis Primer"},{"key":"3426_CR3","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.media.2009.05.005","volume":"13","author":"CI S\u00e1nchez","year":"2009","unstructured":"S\u00e1nchez CI, Garc\u00eda M, Mayo A et al (2009) Retinal image analysis based on mixture models to detect hard exudates. Med Image Anal 13:650\u2013658","journal-title":"Med Image Anal"},{"issue":"12","key":"3426_CR4","doi-asserted-by":"publisher","first-page":"8927","DOI":"10.1109\/TPAMI.2021.3126648","volume":"44","author":"X-S Wei","year":"2022","unstructured":"Wei X-S et al (2022) Fine-grained image analysis with deep learning: a survey. IEEE Trans Pattern Anal Mach Intell 44(12):8927\u20138948","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3426_CR5","doi-asserted-by":"crossref","unstructured":"Srividya K, Joshitha KL (2022) A survey on detection of diabetic retinopathy lesions using deep learning. In: 2022 international conference on communication, computing and internet of things (IC3IoT), Chennai, pp 1\u20136","DOI":"10.1109\/IC3IOT53935.2022.9767907"},{"key":"3426_CR6","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neucom.2018.02.035","volume":"290","author":"J Mo","year":"2018","unstructured":"Mo J, Zhang L, Feng Y (2018) Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks. Neurocomputing 290:161\u2013171","journal-title":"Neurocomputing"},{"key":"3426_CR7","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/978-3-030-27272-2_29","volume-title":"Image analysis and recognition: 16th international conference, ICIAR 2019, Waterloo, ON, Canada, August 27\u201329, 2019, proceedings, part II","author":"Q Xiao","year":"2019","unstructured":"Xiao Q, Zou J, Yang M et al (2019) Improving lesion segmentation for diabetic retinopathy using adversarial learning. In: Image analysis and recognition: 16th international conference, ICIAR 2019, Waterloo, ON, Canada, August 27\u201329, 2019, proceedings, part II. Springer-Verlag, Berlin, Heidelberg, pp 333\u2013344"},{"key":"3426_CR8","first-page":"189","volume-title":"Artificial neural networks and machine learning \u2013 ICANN 2019: image processing. ICANN 2019, vol 11729","author":"S Guo","year":"2019","unstructured":"Guo S, Li T, Wang K et al (2019) A lightweight neural network for hard exudate segmentation of fundus image. In: Artificial neural networks and machine learning \u2013 ICANN 2019: image processing. ICANN 2019, vol 11729. Springer, Cham, pp 189\u2013199"},{"key":"3426_CR9","doi-asserted-by":"crossref","unstructured":"Zhang J, Chen X, Qiu Z et al (2022) Hard exudate segmentation supplemented by super-resolution with multi-scale attention fusion module. In: 2022 IEEE international conference on bioinformatics and biomedicine (BIBM), Las Vegas, NV, pp 1375\u20131380","DOI":"10.1109\/BIBM55620.2022.9995545"},{"key":"3426_CR10","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.neucom.2018.10.103","volume":"392","author":"S Guo","year":"2020","unstructured":"Guo S, Wang K, Kang H et al (2020) Bin loss for hard exudates segmentation in fundus images. Neurocomputing 392:314\u2013324","journal-title":"Neurocomputing"},{"issue":"3","key":"3426_CR11","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1109\/JBHI.2021.3108169","volume":"26","author":"Q Liu","year":"2022","unstructured":"Liu Q, Liu H, Zhao Y et al (2022) Dual-branch network with dual-sampling modulated dice loss for hard exudate segmentation in color fundus images. IEEE J Biomed Health Inform 26(3):1091\u20131102","journal-title":"IEEE J Biomed Health Inform"},{"key":"3426_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2023.120987","volume":"234","author":"Y Fu","year":"2023","unstructured":"Fu Y, Zhang G, Lu X et al (2023) RMCA U-net: Hard exudates segmentation for retinal fundus images. Expert Syst Appl 234:1\u201311","journal-title":"Expert Syst Appl"},{"key":"3426_CR13","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper J, Oktay O, Schaap M et al (2019) Attention gated networks: learning to leverage salient regions in medical images. Med Image Anal 53:197\u2013207","journal-title":"Med Image Anal"},{"key":"3426_CR14","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3426_CR15","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer vision \u2013 ECCV 2018: 15th European conference, Munich, Germany, September 8\u201314, 2018, proceedings, part VII","author":"S Woo","year":"2018","unstructured":"Woo S, Park J, Lee J-Y et al (2018) CBAM: convolutional block attention module. In: Computer vision \u2013 ECCV 2018: 15th European conference, Munich, Germany, September 8\u201314, 2018, proceedings, part VII. Springer-Verlag, Berlin, Heidelberg, pp 3\u201319"},{"key":"3426_CR16","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/978-3-030-00928-1_48","volume-title":"Medical image computing and computer assisted intervention \u2013 MICCAI 2018. MICCAI 2018","author":"AG Roy","year":"2018","unstructured":"Roy AG, Navab N, Wachinger C (2018) Concurrent spatial and channel \u2018squeeze & excitation\u2019 in fully convolutional networks. In: Medical image computing and computer assisted intervention \u2013 MICCAI 2018. MICCAI 2018, vol 11070. Springer, Cham, pp 421\u2013429"},{"key":"3426_CR17","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical image computing and computer-assisted intervention \u2013 MICCAI 2015. MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention \u2013 MICCAI 2015. MICCAI 2015, vol 9351. Springer, Cham, pp 234\u2013241"},{"key":"3426_CR18","doi-asserted-by":"crossref","unstructured":"Huang H, Lin L, Tong R et al (2020) UNet 3+: a full-scale connected UNet for medical image segmentation. In: ICASSP 2020 - 2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), Barcelona, pp 1055\u20131059","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"issue":"6","key":"3426_CR19","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N et al (2020) UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"key":"3426_CR20","doi-asserted-by":"publisher","first-page":"6230","DOI":"10.1109\/CVPR.2017.660","volume-title":"2017 IEEE conference on computer vision and pattern recognition (CVPR)","author":"H Zhao","year":"2017","unstructured":"Zhao H, Shi J, Qi X et al (2017) Pyramid scene parsing network. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). Honolulu, pp 6230\u20136239"},{"key":"3426_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), vol 2015, Boston, MA, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"3426_CR22","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the inception architecture for computer vision. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"3426_CR23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231","author":"C Szegedy","year":"2017","unstructured":"Szegedy C, Ioffe S, Vanhoucke V et al (2017) Inception-v4, Inception-ResNet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence. https:\/\/doi.org\/10.1609\/aaai.v31i1.11231","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"3426_CR24","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer vision \u2013 ECCV 2018. ECCV 2018","author":"LC Chen","year":"2018","unstructured":"Chen LC, Zhu Y, Papandreou G et al (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Computer vision \u2013 ECCV 2018. ECCV 2018, vol 11211. Springer, Cham, pp 833\u2013851"},{"key":"3426_CR25","doi-asserted-by":"crossref","unstructured":"Xie S, Tu Z (2015) Holistically-nested edge detection. In: 2015 IEEE international conference on computer vision (ICCV), Santiago, pp 1395\u20131403","DOI":"10.1109\/ICCV.2015.164"},{"issue":"2","key":"3426_CR26","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1109\/TMI.2020.3035253","volume":"40","author":"R Gu","year":"2021","unstructured":"Gu R, Wang G, Song T et al (2021) Ca-net: comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Trans Med Imaging 40(2):699\u2013711","journal-title":"IEEE Trans Med Imaging"},{"key":"3426_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bspc.2024.106919","volume":"100","author":"H Aghapanah","year":"2025","unstructured":"Aghapanah H, Rasti R, Tabesh F et al (2025) MECardNet: a novel multi-scale convolutional ensemble model with adaptive deep supervision for precise cardiac MRI segmentation. Biomed Signal Process Control 100:1\u201315","journal-title":"Biomed Signal Process Control"},{"key":"3426_CR28","doi-asserted-by":"crossref","unstructured":"Basu S, Mitra S (2021) Segmentation in diabetic retinopathy using deeply-supervised multiscalar attention. In: 2021 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC), Mexico, pp 2614\u20132617","DOI":"10.1109\/EMBC46164.2021.9630600"},{"key":"3426_CR29","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3426_CR30","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","volume":"501","author":"T Li","year":"2019","unstructured":"Li T, Gao Y, Wang K et al (2019) Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Inf Sci 501:511\u2013522","journal-title":"Inf Sci"},{"issue":"3","key":"3426_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/data3030025","volume":"3","author":"P Porwal","year":"2018","unstructured":"Porwal P, Pachade S, Kamble R et al (2018) Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data 3(3):1\u20138","journal-title":"Data"},{"key":"3426_CR32","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.irbm.2013.01.010","volume":"34","author":"E Decenci\u00e8re","year":"2013","unstructured":"Decenci\u00e8re E, Cazuguel G, Zhang X et al (2013) TeleOphta: machine learning and image processing methods for teleophthalmology. IRBM 34:196\u2013203","journal-title":"IRBM"},{"key":"3426_CR33","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.neucom.2019.04.019","volume":"349","author":"S Guo","year":"2019","unstructured":"Guo S, Li T, Kang H et al (2019) L-Seg: an end-to-end unified framework for multi-lesion segmentation of fundus images. Neurocomputing 349:52\u201363","journal-title":"Neurocomputing"},{"key":"3426_CR34","doi-asserted-by":"publisher","first-page":"3146","DOI":"10.1109\/TMI.2022.3177803","volume":"41","author":"A He","year":"2022","unstructured":"He A, Wang K, Li T et al (2022) Progressive multiscale consistent network for multiclass fundus lesion segmentation. IEEE Trans. Med. Imaging 41:3146\u20133157","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3426_CR35","first-page":"1","volume":"105050","author":"T Guo","year":"2023","unstructured":"Guo T, Yang J, Qi Yu (2023) Diabetic retinopathy lesion segmentation using deep multi-scale framework. Biomed Signal Process Cont 105050:1\u201311","journal-title":"Biomed Signal Process Cont"},{"issue":"101561","key":"3426_CR36","first-page":"1","volume":"59","author":"P Porwal","year":"2020","unstructured":"Porwal P, Pachade S, Kokare M et al (2020) IDRiD: Diabetic retinopathy \u2013 segmentation and grading challenge. Med Image Anal 59(101561):1\u201326","journal-title":"Med Image Anal"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03426-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03426-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03426-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T15:32:17Z","timestamp":1764775937000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03426-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,13]]},"references-count":36,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["3426"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03426-7","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2025,8,13]]},"assertion":[{"value":"10 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}