{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T11:58:12Z","timestamp":1781697492963,"version":"3.54.5"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.neucom.2026.133777","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T15:31:48Z","timestamp":1777563108000},"page":"133777","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["InformationGainLoss: An edge-aware and class-balanced loss function for robust semantic segmentation"],"prefix":"10.1016","volume":"689","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6267-2035","authenticated-orcid":false,"given":"Ahmed","family":"Imtiaz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nahar Islam","family":"Nishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"SK Muktadir","family":"Hossain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhijit","family":"Bhowmik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debajyoti","family":"Karmaker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133777_bib0005","author":"Lin"},{"key":"10.1016\/j.neucom.2026.133777_bib0010","series-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","first-page":"240","article-title":"Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations","author":"Sudre","year":"2017"},{"key":"10.1016\/j.neucom.2026.133777_bib0015","series-title":"International Workshop on Machine Learning in Medical Imaging","first-page":"379","article-title":"Tversky loss function for image segmentation using 3D fully convolutional deep networks","author":"Salehi","year":"2017"},{"key":"10.1016\/j.neucom.2026.133777_bib0020","author":"Yu"},{"key":"10.1016\/j.neucom.2026.133777_bib0025","series-title":"2024 Second International Conference on Data Science and Information System (ICDSIS)","first-page":"1","article-title":"Segmentation of brain tumor with deep learning models","author":"Ramadevi","year":"2024"},{"key":"10.1016\/j.neucom.2026.133777_bib0030","article-title":"enhancing image segmentation accuracy using deep learning techniques","author":"Sree","year":"2024","journal-title":"J. Adv. Res. Appl. Sci. Eng. Technol."},{"key":"10.1016\/j.neucom.2026.133777_bib0035","doi-asserted-by":"crossref","DOI":"10.69761\/HDTA8338","article-title":"Deep learning applications in microscopy: segmentation and tracking","author":"Duan","year":"2024","journal-title":"Elem. Microsc."},{"issue":"1","key":"10.1016\/j.neucom.2026.133777_bib0040","doi-asserted-by":"crossref","DOI":"10.1088\/1742-6596\/2833\/1\/012011","article-title":"Research on the application of deep learning in human spinal image segmentation","volume":"2833","author":"Feng","year":"2024","journal-title":"J. Phys. Conf. Ser."},{"key":"10.1016\/j.neucom.2026.133777_bib0045","series-title":"2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII)","first-page":"310","article-title":"research on the development of image segmentation based on deep learning","author":"He","year":"2024"},{"key":"10.1016\/j.neucom.2026.133777_bib0050","series-title":"2024 International Conference on Electrical, Computer and Energy Technologies (ICECET)","first-page":"1","article-title":"Image segmentation survey: classical and deep learning methods","author":"Bachani","year":"2024"},{"key":"10.1016\/j.neucom.2026.133777_bib0055","series-title":"Intelligent Multimedia Processing and Computer Vision: Techniques and Applications","first-page":"7","article-title":"State-of-the-art analysis of deep learning techniques for image segmentation","author":"Yadav","year":"2023"},{"key":"10.1016\/j.neucom.2026.133777_bib0060","series-title":"2023 IEEE 3rd Mysore Sub Section International Conference (MysuruCon)","first-page":"1","article-title":"Segmentation-based classification deep learning model for breast cancer detection using mammogram images","author":"Sinha","year":"2023"},{"key":"10.1016\/j.neucom.2026.133777_bib0065","series-title":"Segmentation of Deep Learning Models","first-page":"12","author":"Kaur","year":"2022"},{"key":"10.1016\/j.neucom.2026.133777_bib0070","doi-asserted-by":"crossref","DOI":"10.17762\/ijritcc.v11i5.6525","article-title":"an efficient data analytics and optimized algorithm for enhancing the performance of image segmentation using deep learning model","author":"Vidyullatha","year":"2023","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"10.1016\/j.neucom.2026.133777_bib0075","author":"Akhmedova"},{"key":"10.1016\/j.neucom.2026.133777_bib0080","series-title":"2024 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","first-page":"1","article-title":"A comparative analysis of loss functions in segmentation of medical images with highly imbalanced class distribution: an experimental study for deep nuclei segmentation","author":"Y\u0131ld\u0131z","year":"2024"},{"key":"10.1016\/j.neucom.2026.133777_bib0085","article-title":"crack segmentation of imbalanced data: the role of loss functions","author":"Du Nguyen","year":"2023","journal-title":"Eng. Struct."},{"key":"10.1016\/j.neucom.2026.133777_bib0090","doi-asserted-by":"crossref","DOI":"10.1002\/ecj.12429","article-title":"loss function for ambiguous boundaries for deep neural network (DNN) for image segmentation","author":"HAKUMURA","year":"2023","journal-title":"Electron. Commun. Jpn."},{"key":"10.1016\/j.neucom.2026.133777_bib0095","article-title":"loss function for ambiguous boundaries for deep neural network (DNN) for image segmentation","author":"HAKUMURA","year":"2023","journal-title":"Trans. Inst. Electr. Eng. Jpn.C"},{"key":"10.1016\/j.neucom.2026.133777_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120183","article-title":"boundary-wise loss for medical image segmentation based on fuzzy rough sets","author":"Lin","year":"2024","journal-title":"Inf. Sci."},{"key":"10.1016\/j.neucom.2026.133777_bib0105","author":"Huang"},{"key":"10.1016\/j.neucom.2026.133777_bib0110","article-title":"a survey of loss function of medical image segmentation algorithms","author":"Chen","year":"2023","journal-title":"Sheng wu yi xue gong cheng xue za zhi = J. biomed. eng. = Shengwu yixue gongchengxue zazhi"},{"key":"10.1016\/j.neucom.2026.133777_bib0115","series-title":"2023 IEEE Guwahati Subsection Conference (GCON)","first-page":"1","article-title":"Improving semantic segmentation performance of deep networks for autonomous driving through loss functions","author":"Mazhar","year":"2023"},{"key":"10.1016\/j.neucom.2026.133777_bib0120","series-title":"Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing","first-page":"743","article-title":"Automated image segmentation using improved PCNN model based on cross-entropy","author":"Yi-de","year":"2004"},{"key":"10.1016\/j.neucom.2026.133777_bib0125","article-title":"Uncertainty-aware cross-entropy for semantic segmentation","author":"Landgraf","year":"2024","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"10.1016\/j.neucom.2026.133777_bib0130","series-title":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","article-title":"The combined focal cross entropy and dice loss function for segmentation of protein secondary structures from cryo-em 3D density maps","author":"Mu","year":"2022"},{"key":"10.1016\/j.neucom.2026.133777_bib0135","series-title":"On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness","author":"Galdran","year":"2023"},{"key":"10.1016\/j.neucom.2026.133777_bib0140","author":"Galdran"},{"issue":"1","key":"10.1016\/j.neucom.2026.133777_bib0145","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1785\/0320240028","article-title":"Reducing the parameter dependency of phase-picking neural networks with dice loss","volume":"5","author":"Park","year":"2025","journal-title":"Seism. Rec."},{"key":"10.1016\/j.neucom.2026.133777_bib0150","doi-asserted-by":"crossref","DOI":"10.1093\/bioadv\/vbae169","article-title":"The combined focal loss and dice loss function improves the segmentation of beta-sheets in medium-resolution cryo-electron-microscopy density maps","author":"Mu","year":"2024","journal-title":"Bioinform. Adv."},{"key":"10.1016\/j.neucom.2026.133777_bib0155","author":"Shi"},{"key":"10.1016\/j.neucom.2026.133777_bib0160","article-title":"Adaptive t-vmf dice loss: an effective expansion of dice loss for medical image segmentation","volume":"168","author":"Kato","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neucom.2026.133777_bib0165","author":"Spratling"},{"key":"10.1016\/j.neucom.2026.133777_bib0170","author":"Islam"},{"key":"10.1016\/j.neucom.2026.133777_bib0175","article-title":"Improving calibration by relating focal loss, temperature scaling, and properness","author":"Komisarenko","year":"2024","journal-title":"Front. artif. intell. appl."},{"key":"10.1016\/j.neucom.2026.133777_bib0180","author":"Kimura"},{"key":"10.1016\/j.neucom.2026.133777_bib0185","author":"Komisarenko"},{"key":"10.1016\/j.neucom.2026.133777_bib0190","article-title":"Autonomous intersection over union (IOU) loss: adaptive dynamic non-monotonic focal IOU loss","volume":"10","author":"Zhu","year":"2024","journal-title":"Peer J."},{"issue":"13","key":"10.1016\/j.neucom.2026.133777_bib0195","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1049\/icp.2024.2884","article-title":"Particle-ellipse IOU loss for accurate drogue detection","volume":"2024","author":"Tan","year":"2025","journal-title":"IET conf. proc."},{"key":"10.1016\/j.neucom.2026.133777_bib0200","article-title":"Apd-yolov7: enhancing sustainable farming through precise identification of agricultural pests and diseases using a novel diagonal difference ratio IOU loss","author":"Li","year":"2024","journal-title":"Sustainability"},{"key":"10.1016\/j.neucom.2026.133777_bib0205","series-title":"Relation-Iou: A Novel Bounding Box Regression Loss for Early Apple Disease Detection","author":"Ren","year":"2024"},{"key":"10.1016\/j.neucom.2026.133777_bib0210","doi-asserted-by":"crossref","DOI":"10.3390\/fi15120399","article-title":"Addressing the gaps of IOU loss in 3D object detection with iiou","author":"Ravi","year":"2023","journal-title":"Future Internet"},{"key":"10.1016\/j.neucom.2026.133777_bib0215","series-title":"A Novel Focal Tversky Loss Function with Improved Attention U-Net for Lesion Segmentation","author":"Abraham","year":"2023"},{"key":"10.1016\/j.neucom.2026.133777_bib0220","series-title":"2021 IEEE International Conference on Image Processing (ICIP)","first-page":"2209","article-title":"Layer-wise customized weak segmentation block and aiou loss for accurate object detection","author":"Wang","year":"2021"},{"key":"10.1016\/j.neucom.2026.133777_bib0225","series-title":"2024 3rd International Conference on Image Processing and Media Computing (ICIPMC)","first-page":"291","article-title":"Relation-iou: a novel bounding box regression loss for early Apple disease detection","author":"Ren","year":"2024"},{"key":"10.1016\/j.neucom.2026.133777_bib0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101851","article-title":"Boundary loss for highly unbalanced segmentation","volume":"67","author":"Kervadec","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2026.133777_bib0235","author":"Landgraf"},{"key":"10.1016\/j.neucom.2026.133777_bib0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120183","article-title":"Boundary-wise loss for medical image segmentation based on fuzzy rough sets","volume":"661","author":"Lin","year":"2024","journal-title":"Inf. Sci."},{"key":"10.1016\/j.neucom.2026.133777_bib0245","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"3213","article-title":"The cityscapes dataset for semantic urban scene understanding","author":"Cordts","year":"2016"},{"key":"10.1016\/j.neucom.2026.133777_bib0250","series-title":"ECCV (1)","first-page":"44","article-title":"Segmentation and recognition using structure from motion point clouds","author":"Brostow","year":"2008"},{"key":"10.1016\/j.neucom.2026.133777_bib0255","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","first-page":"172","article-title":"Deepglobe 2018: a challenge to parse the earth through satellite images","author":"Demir","year":"2018"},{"key":"10.1016\/j.neucom.2026.133777_bib0260","author":"Abid"},{"key":"10.1016\/j.neucom.2026.133777_bib0265","author":"Ronneberger"},{"issue":"2","key":"10.1016\/j.neucom.2026.133777_bib0270","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/S1076-6332(03)00671-8","article-title":"Statistical validation of image segmentation quality based on a spatial overlap index","volume":"11","author":"Zou","year":"2004","journal-title":"Acad. Radiol."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226011744?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226011744?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T11:32:42Z","timestamp":1781695962000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226011744"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":54,"alternative-id":["S0925231226011744"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133777","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"InformationGainLoss: An edge-aware and class-balanced loss function for robust semantic segmentation","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133777","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133777"}}