{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:25:03Z","timestamp":1783124703625,"version":"3.54.6"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007957","name":"Chongqing Municipal Education Commission","doi-asserted-by":"publisher","award":["KJZD-K202300604"],"award-info":[{"award-number":["KJZD-K202300604"]}],"id":[{"id":"10.13039\/501100007957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004502","name":"Chongqing University of Posts and Telecommunications","doi-asserted-by":"publisher","award":["BYJS202410"],"award-info":[{"award-number":["BYJS202410"]}],"id":[{"id":"10.13039\/501100004502","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX0547"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0547"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["CSTB2023NSCQ-LZX0061"],"award-info":[{"award-number":["CSTB2023NSCQ-LZX0061"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["CSTB2024NSCQ-QCXMX0060"],"award-info":[{"award-number":["CSTB2024NSCQ-QCXMX0060"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX1024"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX1024"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M720548"],"award-info":[{"award-number":["2022M720548"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFA1004100"],"award-info":[{"award-number":["2022YFA1004100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472060"],"award-info":[{"award-number":["62472060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20318"],"award-info":[{"award-number":["U23A20318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62221005"],"award-info":[{"award-number":["62221005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2096"],"award-info":[{"award-number":["U22A2096"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102057"],"award-info":[{"award-number":["62102057"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.neucom.2026.134239","type":"journal-article","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T15:32:15Z","timestamp":1780759935000},"page":"134239","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["TumorAL: Evidence-aware active learning for 3D tumor segmentation"],"prefix":"10.1016","volume":"697","author":[{"given":"Hongyi","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxu","family":"Leng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weikai","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9033-8245","authenticated-orcid":false,"given":"Weisheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinbo","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.134239_bib0005","article-title":"Masked graph convolutional neural network for medical image segmentation with anatomical priors","author":"Li","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134239_bib0010","article-title":"MTCL: multi-task consistency learning for semi-supervised 3D medical image segmentation","author":"Hu","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134239_bib0015","article-title":"TransCapsUNet: a transformer-capsule integrated U-Net for 3D volumetric medical image segmentation","author":"Vatani","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134239_bib0020","article-title":"Superpixel-based hypergraph neural networks with active learning for efficient hyperspectral image classification","author":"Qin","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134239_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110731","article-title":"Dynamic weighted knowledge distillation for brain tumor segmentation","volume":"155","author":"An","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.134239_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109817","article-title":"Deep active learning models for imbalanced image classification","volume":"257","author":"Jin","year":"2022","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.neucom.2026.134239_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103201","article-title":"A comprehensive survey on deep active learning in medical image analysis","volume":"95","author":"Wang","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2026.134239_bib0040","series-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1531","article-title":"Breaking the barrier: selective uncertainty-based active learning for medical image segmentation","author":"Ma","year":"2024"},{"issue":"10","key":"10.1016\/j.neucom.2026.134239_bib0045","doi-asserted-by":"crossref","first-page":"3744","DOI":"10.1109\/JBHI.2021.3052320","article-title":"Dsal: deeply supervised active learning from strong and weak labelers for biomedical image segmentation","volume":"25","author":"Zhao","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"10","key":"10.1016\/j.neucom.2026.134239_bib0050","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/TMI.2020.3048055","article-title":"Diminishing uncertainty within the training pool: active learning for medical image segmentation","volume":"40","author":"Nath","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.134239_bib0055","series-title":"MICCAI Workshop on Data Augmentation, Labelling, and Imperfections","first-page":"43","article-title":"Taal: test-time augmentation for active learning in medical image segmentation","author":"Gaillochet","year":"2022"},{"key":"10.1016\/j.neucom.2026.134239_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130265","article-title":"Uncertainty and diversity-based active learning for UAV tracking","author":"Liang","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134239_bib0065","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"244","article-title":"Self-learning and one-shot learning based single-slice annotation for 3D medical image segmentation","author":"Wu","year":"2022"},{"key":"10.1016\/j.neucom.2026.134239_bib0070","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"25","article-title":"COLosSAL: a benchmark for cold-start active learning for 3D medical image segmentation","author":"Liu","year":"2023"},{"key":"10.1016\/j.neucom.2026.134239_bib0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130083","article-title":"SiameseDuo++: active learning from data streams with dual augmented Siamese networks","volume":"637","author":"Malialis","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134239_bib0080","series-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","first-page":"3183","article-title":"Evidential deep learning to quantify classification uncertainty","author":"Sensoy","year":"2018"},{"key":"10.1016\/j.neucom.2026.134239_bib0085","article-title":"Switch-UMamba: dynamic scanning vision Mamba UNet for medical image segmentation","author":"Zhang","year":"2025","journal-title":"Med. Image Anal."},{"issue":"2","key":"10.1016\/j.neucom.2026.134239_bib0090","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/TMI.2023.3317088","article-title":"MTANet: multi-task attention network for automatic medical image segmentation and classification","volume":"43","author":"Ling","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"10.1016\/j.neucom.2026.134239_bib0095","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 Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2026.134239_bib0100","article-title":"MGC-net: semi-supervised domain generalization in medical image segmentation via multi-granularity consistency","author":"Li","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134239_bib0105","doi-asserted-by":"crossref","first-page":"5761","DOI":"10.1109\/TIP.2025.3607615","article-title":"MambaDiff: Mamba-enhanced diffusion model for 3D medical image segmentation","volume":"34","author":"Liu","year":"2025","journal-title":"IEEE Trans. Image Process."},{"issue":"10","key":"10.1016\/j.neucom.2026.134239_bib0110","doi-asserted-by":"crossref","first-page":"3898","DOI":"10.1109\/TMI.2024.3508698","article-title":"Swin-UMamba\u2020: adapting Mamba-based vision foundation models for medical image segmentation","volume":"44","author":"Liu","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.134239_bib0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103554","article-title":"MedScale-Former: self-guided multiscale transformer for medical image segmentation","volume":"103","author":"Karimijafarbigloo","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2026.134239_bib0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111378","article-title":"Lite-MixedNet: lightweight and efficient hybrid network for medical image segmentation","volume":"162","author":"Ren","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.134239_bib0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103545","article-title":"LW-CTrans: a lightweight hybrid network of CNN and transformer for 3D medical image segmentation","volume":"102","author":"Kuang","year":"2025","journal-title":"Med. Image Anal."},{"issue":"9","key":"10.1016\/j.neucom.2026.134239_bib0130","doi-asserted-by":"crossref","first-page":"6740","DOI":"10.1109\/JBHI.2025.3561425","article-title":"MIT-SAM: medical image-text SAM with mutually enhanced heterogeneous features fusion for medical image segmentation","volume":"29","author":"Zhou","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"10","key":"10.1016\/j.neucom.2026.134239_bib0135","doi-asserted-by":"crossref","first-page":"17693","DOI":"10.1109\/TNNLS.2025.3568479","article-title":"Retrieval-augmented few-shot medical image segmentation with foundation models","volume":"36","author":"Zhao","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2026.134239_bib0140","article-title":"MFHS: mutual consistency learning-based foundation model integrates hypergraph for semi-supervised medical image segmentation","author":"Liu","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.134239_bib0145","doi-asserted-by":"crossref","first-page":"3169","DOI":"10.1109\/TIP.2025.3571672","article-title":"STPNet: scale-aware text prompt network for medical image segmentation","volume":"34","author":"Shan","year":"2025","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.neucom.2026.134239_bib0150","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11632","article-title":"Learning active contour models for medical image segmentation","author":"Chen","year":"2019"},{"key":"10.1016\/j.neucom.2026.134239_bib0155","article-title":"Active learning on a budget: opposite strategies suit high and low budgets","volume":"abs\/2202.02794","author":"Hacohen","year":"2022","journal-title":"Int. Conf. Mach. Learn."},{"key":"10.1016\/j.neucom.2026.134239_bib0160","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10988","article-title":"Revisiting superpixels for active learning in semantic segmentation with realistic annotation costs","author":"Cai","year":"2021"},{"key":"10.1016\/j.neucom.2026.134239_bib0165","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.neucom.2021.03.066","article-title":"An evidential classifier based on Dempster Shafer theory and deep learning","volume":"450","author":"Tong","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134239_bib0170","series-title":"2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)","first-page":"865","article-title":"DEAL: deep evidential active learning for image classification","author":"Hemmer","year":"2020"},{"issue":"30","key":"10.1016\/j.neucom.2026.134239_bib0175","doi-asserted-by":"crossref","first-page":"22071","DOI":"10.1007\/s00521-022-08016-4","article-title":"Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation","volume":"35","author":"Li","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.neucom.2026.134239_bib0180","first-page":"1","article-title":"Uncertainty-aware health diagnostics via class-balanced evidential deep learning","author":"Xia","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.neucom.2026.134239_bib0185","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11439","article-title":"Think twice before selection: federated evidential active learning for medical image analysis with domain shifts","author":"Chen","year":"2024"},{"issue":"1","key":"10.1016\/j.neucom.2026.134239_bib0190","doi-asserted-by":"crossref","first-page":"4128","DOI":"10.1038\/s41467-022-30695-9","article-title":"The medical segmentation decathlon","volume":"13","author":"Antonelli","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.neucom.2026.134239_bib0195","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"297","article-title":"Warm start active learning with proxy labels and selection via semi-supervised fine-tuning","author":"Nath","year":"2022"},{"key":"10.1016\/j.neucom.2026.134239_bib0200","series-title":"International Conference on Machine Learning","first-page":"1183","article-title":"Deep Bayesian active learning with image data","author":"Gal","year":"2017"},{"key":"10.1016\/j.neucom.2026.134239_bib0205","series-title":"6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings","article-title":"Active learning for convolutional neural networks: a core-set approach","author":"Sener","year":"2018"},{"key":"10.1016\/j.neucom.2026.134239_bib0210","series-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17\u201324, 2023","first-page":"23715","article-title":"Active finetuning: exploiting annotation budget in the pretraining-finetuning paradigm","author":"Xie","year":"2023"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226016371?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226016371?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:03:09Z","timestamp":1783123389000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226016371"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":42,"alternative-id":["S0925231226016371"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134239","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"TumorAL: Evidence-aware active learning for 3D tumor segmentation","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134239","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":"134239"}}