{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T21:05:00Z","timestamp":1790111100982,"version":"4.0.1"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"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":["Neural Networks"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.neunet.2026.109409","type":"journal-article","created":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T15:24:15Z","timestamp":1784388255000},"page":"109409","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"PA","title":["DCL-SE: Dynamic curriculum learning for spatiotemporal encoding of brain imaging"],"prefix":"10.1016","volume":"205","author":[{"given":"Meihua","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiarui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.109409_bib0001","series-title":"Proc. 26th Annual Int. Conf. Machine Learning","first-page":"41","article-title":"Curriculum learning","author":"Bengio","year":"2009"},{"issue":"12","key":"10.1016\/j.neunet.2026.109409_bib0002","doi-asserted-by":"crossref","first-page":"2799","DOI":"10.1109\/TPAMI.2017.2769085","article-title":"Action recognition with dynamic image networks","volume":"40","author":"Bilen","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109409_bib0003","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"3034","article-title":"Dynamic image networks for action recognition","author":"Bilen","year":"2016"},{"key":"10.1016\/j.neunet.2026.109409_bib0004","series-title":"Proc. 2021\u202fIEEE Int. Conf. Image Processing (ICIP)","first-page":"3787","article-title":"Unsupervised medical image alignment with curriculum learning","author":"Burduja","year":"2021"},{"key":"10.1016\/j.neunet.2026.109409_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.108257","article-title":"LCMF-Net: A lightweight collaborative multimodal fusion network for brain tumor segmentation","volume":"195","author":"Cao","year":"2026","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109409_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103773","article-title":"Label-conditioned multi-GAN fusion: A robust data augmentation strategy for medical image segmentation","volume":"127","author":"Chen","year":"2026","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109409_bib0007","series-title":"Proc. 2018\u202fIEEE symposium on computer applications & industrial electronics (ISCAIE)","first-page":"310","article-title":"Human brain modeling tumor detection in 2D and 3D representation using microwave signal analysis","author":"Chew","year":"2018"},{"issue":"20_Supplement_2","key":"10.1016\/j.neunet.2026.109409_bib0008","doi-asserted-by":"crossref","first-page":"S42","DOI":"10.1212\/WNL.0000000000012794","article-title":"Overview of imaging modalities in stroke","volume":"97","author":"Czap","year":"2021","journal-title":"Neurology"},{"key":"10.1016\/j.neunet.2026.109409_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107684","article-title":"A classification method of motor imagery based on brain functional networks by fusing PLV and ECSP","volume":"190","author":"Fan","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109409_bib0010","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.neunet.2021.04.029","article-title":"Classification of visuomotor tasks based on electroencephalographic data depends on age-related differences in brain activity patterns","volume":"142","author":"Goelz","year":"2021","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109409_bib0011","series-title":"Proc. 2024\u202fIEEE Int. Conf. Advanced Systems and Emergent Technologies (IC_ASET)","first-page":"1","article-title":"Multimodal MRI brain tumor segmentation using 3D and 3D\/2D methods: A study on the MICCAI BRATS dataset","author":"Gtifa","year":"2024"},{"issue":"1","key":"10.1016\/j.neunet.2026.109409_bib0012","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s40537-022-00652-w","article-title":"Transfer learning: A friendly introduction","volume":"9","author":"Hosna","year":"2022","journal-title":"Journal of Big Data"},{"issue":"9","key":"10.1016\/j.neunet.2026.109409_bib0013","doi-asserted-by":"crossref","first-page":"5280","DOI":"10.1109\/JBHI.2024.3407116","article-title":"Accurate whole-brain image enhancement for low-dose integrated PET\/MR imaging through spatial brain transformation","volume":"28","author":"Huang","year":"2024","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.1016\/j.neunet.2026.109409_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105534","article-title":"HIFuse: Hierarchical multi-scale feature fusion network for medical image classification","volume":"87","author":"Huo","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"issue":"1","key":"10.1016\/j.neunet.2026.109409_bib0015","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.bbe.2021.11.004","article-title":"Transfer learning techniques for medical image analysis: A review","volume":"42","author":"Kora","year":"2022","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"10.1016\/j.neunet.2026.109409_bib0016","series-title":"Proc. 2019\u202fIEEE 16th Int. Symp. Biomedical Imaging (ISBI 2019)","first-page":"1878","article-title":"Segmentation and recovery of pathological MR brain images using transformed low-rank and structured sparse decomposition","author":"Lin","year":"2019"},{"key":"10.1016\/j.neunet.2026.109409_bib0017","series-title":"Proc. IEEE\/CVF int. conf. comput. vis.","first-page":"10012","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.neunet.2026.109409_bib0018","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nature Communications"},{"issue":"9","key":"10.1016\/j.neunet.2026.109409_bib0019","doi-asserted-by":"crossref","first-page":"3732","DOI":"10.1109\/TNNLS.2019.2934906","article-title":"Teacher-student curriculum learning","volume":"31","author":"Matiisen","year":"2019","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"10.1016\/j.neunet.2026.109409_bib0020","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1109\/TCYB.2018.2797905","article-title":"3-D fully convolutional networks for multimodal isointense infant brain image segmentation","volume":"49","author":"Nie","year":"2019","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.1016\/j.neunet.2026.109409_bib0021","article-title":"Dinov2: Learning robust visual features without supervision","author":"Oquab","year":"2024","journal-title":"Transactions on Machine Learning Research"},{"issue":"4","key":"10.1016\/j.neunet.2026.109409_bib0022","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1109\/TCSVT.2019.2900467","article-title":"Active transfer learning","volume":"30","author":"Peng","year":"2020","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"1","key":"10.1016\/j.neunet.2026.109409_bib0023","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1146\/annurev.neuro.29.051605.112819","article-title":"Brain work and brain imaging","volume":"29","author":"Raichle","year":"2006","journal-title":"Annual Review of Neuroscience"},{"issue":"1","key":"10.1016\/j.neunet.2026.109409_bib0024","doi-asserted-by":"crossref","first-page":"513","DOI":"10.11591\/ijeecs.v27.i1.pp513-520","article-title":"Converting 2D magnetic resource imagining brain tumors to 3D structure using depth map machine learning techniques","volume":"27","author":"Riyazudeen","year":"2022","journal-title":"Indonesian Journal of Electrical Engineering and Computer Science"},{"key":"10.1016\/j.neunet.2026.109409_bib0025","series-title":"Proc. 2015 third Int. Conf. computer, communication, control and information technology (c3IT)","first-page":"1","article-title":"A useful approach towards 3D representation of brain abnormality from its 2D MRI slides with a volumetric exclamation","author":"Roy","year":"2015"},{"issue":"10","key":"10.1016\/j.neunet.2026.109409_bib0026","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/TMI.2018.2824243","article-title":"Multi-atlas segmentation of MR tumor brain images using low-rank based image recovery","volume":"37","author":"Tang","year":"2018","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.neunet.2026.109409_bib0027","series-title":"Proc. 2023 3rd int. conf. innovative sustainable computational technologies (CISCT)","first-page":"1","article-title":"CONVNext: A contemporary architecture for convolutional neural networks for image classification","author":"Todi","year":"2023"},{"key":"10.1016\/j.neunet.2026.109409_bib0028","series-title":"Recent trends in image processing and pattern recognition: Second Int. Conf., RTIP2r 2018, Solapur, India, Dec. 21\u201322, 2018, revised selected papers, Part II 2","first-page":"154","article-title":"A systematic approach for constructing 3D MRI brain image over 2D images","author":"Vidhya","year":"2019"},{"issue":"7","key":"10.1016\/j.neunet.2026.109409_bib0029","doi-asserted-by":"crossref","first-page":"10018","DOI":"10.1109\/TNNLS.2023.3238381","article-title":"GRENet: Gradually recurrent network with curriculum learning for 2-D medical image segmentation","volume":"35","author":"Wang","year":"2024","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.109409_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104858","article-title":"2.75 D: Boosting learning by representing 3D medical imaging to 2D features for small data","volume":"84","author":"Wang","year":"2023","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.neunet.2026.109409_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103861","article-title":"A medical image semantic segmentation method based on multi-color space information fusion strategy","volume":"127","author":"Wang","year":"2026","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109409_bib0032","series-title":"Proc. Conf. Empir. Methods Nat. Lang. Process.","first-page":"3876","article-title":"MedCLIP: Contrastive learning from unpaired medical images and text","author":"Wang","year":"2022"},{"issue":"5","key":"10.1016\/j.neunet.2026.109409_bib0033","doi-asserted-by":"crossref","first-page":"4011","DOI":"10.1109\/TCSVT.2024.3516705","article-title":"ECINFusion: A novel explicit channel-wise interaction network for unified multi-modal medical image fusion","volume":"35","author":"Wei","year":"2025","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.neunet.2026.109409_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103306","article-title":"LPM-Net: Lightweight pixel-level modeling network based on CNN and mamba for 3D medical image fusion","author":"Wen","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109409_bib0035","series-title":"Proc. 2020\u202fIEEE int. conf. bioinformatics and biomedicine (BIBM)","first-page":"827","article-title":"On the effective transfer learning strategy for medical image analysis in deep learning","author":"Wen","year":"2020"},{"key":"10.1016\/j.neunet.2026.109409_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107828","article-title":"ATT-BrainNet: Attention-based brainnet for lung cancer segmentation network","volume":"191","author":"Xiao","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109409_bib0037","series-title":"Computer vision \u2013 ECCV 2020 workshops","first-page":"355","article-title":"Dynamic image for 3D MRI image Alzheimer\u2019s disease classification","author":"Xing","year":"2020"},{"issue":"9","key":"10.1016\/j.neunet.2026.109409_bib0038","doi-asserted-by":"crossref","first-page":"5916","DOI":"10.1109\/TCSVT.2022.3164190","article-title":"Progressive meta-learning with curriculum","volume":"32","author":"Zhang","year":"2022","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.neunet.2026.109409_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109517","article-title":"DAMNet: Dynamic mobile architectures for alzheimer\u2019s disease","volume":"185","author":"Zhou","year":"2025","journal-title":"Computers in Biology and Medicine"},{"issue":"1","key":"10.1016\/j.neunet.2026.109409_bib0040","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proceedings of the IEEE"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026008671?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026008671?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T20:06:15Z","timestamp":1790107575000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026008671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":40,"alternative-id":["S0893608026008671"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109409","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DCL-SE: Dynamic curriculum learning for spatiotemporal encoding of brain imaging","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109409","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109409"}}