{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T17:00:13Z","timestamp":1780938013259,"version":"3.54.1"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015166","name":"UPMC","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.113973","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T06:46:34Z","timestamp":1779518794000},"page":"113973","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Enhancing weakly supervised 3D medical image segmentation through probabilistic-aware learning"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1645-4610","authenticated-orcid":false,"given":"Runmin","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoxin","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7170-471X","authenticated-orcid":false,"given":"Junhao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6263-4716","authenticated-orcid":false,"given":"Lenghan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3184-0566","authenticated-orcid":false,"given":"Tianyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0881-5891","authenticated-orcid":false,"given":"Min","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.113973_b1","series-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.patcog.2026.113973_b2","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.patcog.2026.113973_b3","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"14","article-title":"Transfuse: Fusing transformers and cnns for medical image segmentation","author":"Zhang","year":"2021"},{"key":"10.1016\/j.patcog.2026.113973_b4","series-title":"2016 Fourth International Conference on 3D Vision (3DV)","first-page":"565","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.patcog.2026.113973_b5","first-page":"2441","article-title":"Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer","volume":"vol. 36","author":"Wang","year":"2022"},{"key":"10.1016\/j.patcog.2026.113973_b6","series-title":"European Conference on Computer Vision","first-page":"205","article-title":"Swin-unet: Unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022"},{"key":"10.1016\/j.patcog.2026.113973_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101693","article-title":"Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation","volume":"63","author":"Tajbakhsh","year":"2020","journal-title":"Med. Image Anal."},{"issue":"3","key":"10.1016\/j.patcog.2026.113973_b8","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/3446776","article-title":"Understanding deep learning (still) requires rethinking generalization","volume":"64","author":"Zhang","year":"2021","journal-title":"Commun. ACM"},{"key":"10.1016\/j.patcog.2026.113973_b9","doi-asserted-by":"crossref","unstructured":"E. Panfilov, A. Tiulpin, S. Klein, M.T. Nieminen, S. Saarakkala, Improving robustness of deep learning based knee MRI segmentation: Mixup and adversarial domain adaptation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, 2019.","DOI":"10.1109\/ICCVW.2019.00057"},{"key":"10.1016\/j.patcog.2026.113973_b10","doi-asserted-by":"crossref","unstructured":"C. Fu, S. Lee, D. Joon Ho, S. Han, P. Salama, K.W. Dunn, E.J. Delp, Three dimensional fluorescence microscopy image synthesis and segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 2221\u20132229.","DOI":"10.1109\/CVPRW.2018.00298"},{"key":"10.1016\/j.patcog.2026.113973_b11","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"128","article-title":"Neural style transfer improves 3D cardiovascular MR image segmentation on inconsistent data","author":"Ma","year":"2019"},{"key":"10.1016\/j.patcog.2026.113973_b12","series-title":"Transfer learning with edge attention for prostate MRI segmentation","author":"Qin","year":"2019"},{"key":"10.1016\/j.patcog.2026.113973_b13","series-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"1217","article-title":"Adversarial synthesis learning enables segmentation without target modality ground truth","author":"Huo","year":"2018"},{"key":"10.1016\/j.patcog.2026.113973_b14","series-title":"International Workshop on Statistical Atlases and Computational Models of the Heart","first-page":"209","article-title":"Unsupervised multi-modal style transfer for cardiac MR segmentation","author":"Chen","year":"2019"},{"key":"10.1016\/j.patcog.2026.113973_b15","article-title":"Fully convolutional networks for panoptic segmentation with point-based supervision","author":"Li","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113973_b16","series-title":"European Conference on Computer Vision","first-page":"549","article-title":"What\u2019s the point: Semantic segmentation with point supervision","author":"Bearman","year":"2016"},{"key":"10.1016\/j.patcog.2026.113973_b17","doi-asserted-by":"crossref","unstructured":"D. Lin, J. Dai, J. Jia, K. He, J. Sun, Scribblesup: Scribble-supervised convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 3159\u20133167.","DOI":"10.1109\/CVPR.2016.344"},{"key":"10.1016\/j.patcog.2026.113973_b18","series-title":"International Conference on Machine Learning","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"Gal","year":"2016"},{"key":"10.1016\/j.patcog.2026.113973_b19","doi-asserted-by":"crossref","unstructured":"J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"2","key":"10.1016\/j.patcog.2026.113973_b20","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/JBHI.2019.2912935","article-title":"Fully dense unet for 2-D sparse photoacoustic tomography artifact removal","volume":"24","author":"Guan","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.patcog.2026.113973_b21","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"10.1016\/j.patcog.2026.113973_b22","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"424","article-title":"3D U-net: learning dense volumetric segmentation from sparse annotation","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"10.1016\/j.patcog.2026.113973_b23","doi-asserted-by":"crossref","unstructured":"S. Shabani, S. Mohammed, B. Parvin, A novel 3D decoder with weighted and learnable triple attention for 3D microscopy image segmentation, in: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 4699\u20134708.","DOI":"10.1109\/CVPRW67362.2025.00456"},{"key":"10.1016\/j.patcog.2026.113973_b24","doi-asserted-by":"crossref","unstructured":"S. Kumari, P. Singh, Annotation ambiguity aware semi-supervised medical image segmentation, in: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 10404\u201310413.","DOI":"10.1109\/CVPR52734.2025.00973"},{"issue":"3","key":"10.1016\/j.patcog.2026.113973_b25","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"\u201d GrabCut\u201d interactive foreground extraction using iterated graph cuts","volume":"23","author":"Rother","year":"2004","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.patcog.2026.113973_b26","doi-asserted-by":"crossref","unstructured":"J. Dai, K. He, J. Sun, Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation, in: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1635\u20131643.","DOI":"10.1109\/ICCV.2015.191"},{"key":"10.1016\/j.patcog.2026.113973_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2020.101998","article-title":"Interactive medical image segmentation via a point-based interaction","volume":"111","author":"Zhang","year":"2021","journal-title":"Artif. Intell. Med."},{"issue":"2","key":"10.1016\/j.patcog.2026.113973_b28","doi-asserted-by":"crossref","first-page":"507","DOI":"10.3390\/make3020026","article-title":"Going to extremes: weakly supervised medical image segmentation","volume":"3","author":"Roth","year":"2021","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"10.1016\/j.patcog.2026.113973_b29","series-title":"Pseudoseg: Designing pseudo labels for semantic segmentation","author":"Zou","year":"2020"},{"key":"10.1016\/j.patcog.2026.113973_b30","doi-asserted-by":"crossref","unstructured":"F. Liu, Y. Tian, Y. Chen, Y. Liu, V. Belagiannis, G. Carneiro, ACPL: Anti-Curriculum Pseudo-Labelling for Semi-Supervised Medical Image Classification, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20697\u201320706.","DOI":"10.1109\/CVPR52688.2022.02004"},{"key":"10.1016\/j.patcog.2026.113973_b31","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"25982","article-title":"Incomplete multi-modal brain tumor segmentation via learnable sorting state space model","author":"Zhang","year":"2025"},{"key":"10.1016\/j.patcog.2026.113973_b32","first-page":"4074","article-title":"Dynamic optimization of neural network structures using probabilistic modeling","volume":"vol. 32","author":"Shirakawa","year":"2018"},{"key":"10.1016\/j.patcog.2026.113973_b33","doi-asserted-by":"crossref","unstructured":"J. Choi, I. Elezi, H.-J. Lee, C. Farabet, J.M. Alvarez, Active learning for deep object detection via probabilistic modeling, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 10264\u201310273.","DOI":"10.1109\/ICCV48922.2021.01010"},{"key":"10.1016\/j.patcog.2026.113973_b34","series-title":"International Conference on Machine Learning","first-page":"12413","article-title":"Bayesian attention belief networks","author":"Zhang","year":"2021"},{"key":"10.1016\/j.patcog.2026.113973_b35","doi-asserted-by":"crossref","unstructured":"H. Guo, H. Wang, Q. Ji, Uncertainty-Guided Probabilistic Transformer for Complex Action Recognition, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20052\u201320061.","DOI":"10.1109\/CVPR52688.2022.01942"},{"key":"10.1016\/j.patcog.2026.113973_b36","article-title":"Variational dropout and the local reparameterization trick","volume":"28","author":"Kingma","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113973_b37","doi-asserted-by":"crossref","unstructured":"A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H.R. Roth, D. Xu, UNETR: Transformers for 3D Medical Image Segmentation, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 574\u2013584.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"10.1016\/j.patcog.2026.113973_b38","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113973_b39","first-page":"12","article-title":"Miccai multi-atlas labeling beyond the cranial vault\u2013workshop and challenge","volume":"vol. 5","author":"Landman","year":"2015"},{"key":"10.1016\/j.patcog.2026.113973_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101950","article-title":"CHAOS challenge-combined (CT-mr) healthy abdominal organ segmentation","volume":"69","author":"Kavur","year":"2021","journal-title":"Med. Image Anal."},{"issue":"4","key":"10.1016\/j.patcog.2026.113973_b41","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1145\/235815.235821","article-title":"The quickhull algorithm for convex hulls","volume":"22","author":"Barber","year":"1996","journal-title":"ACM Trans. Math. Softw. (TOMS)"},{"key":"10.1016\/j.patcog.2026.113973_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102385","article-title":"Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels","volume":"78","author":"Hansen","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2026.113973_b43","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2022.3150682","article-title":"Self-supervised learning for few-shot medical image segmentation","author":"Ouyang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.113973_b44","series-title":"International MICCAI Brainlesion Workshop","first-page":"272","article-title":"Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images","author":"Hatamizadeh","year":"2021"},{"key":"10.1016\/j.patcog.2026.113973_b45","first-page":"326","article-title":"Dynamic U-net: Adaptively calibrate features for abdominal multiorgan segmentation","volume":"vol. 13407","author":"Yang","year":"2025"},{"key":"10.1016\/j.patcog.2026.113973_b46","doi-asserted-by":"crossref","unstructured":"R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"10.1016\/j.patcog.2026.113973_b47","doi-asserted-by":"crossref","unstructured":"H. Wang, Z. Wang, M. Du, F. Yang, Z. Zhang, S. Ding, P. Mardziel, X. Hu, Score-CAM: Score-weighted visual explanations for convolutional neural networks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 24\u201325.","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"10.1016\/j.patcog.2026.113973_b48","doi-asserted-by":"crossref","unstructured":"K.H. Lee, C. Park, J. Oh, N. Kwak, LFI-CAM: Learning feature importance for better visual explanation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 1355\u20131363.","DOI":"10.1109\/ICCV48922.2021.00139"},{"key":"10.1016\/j.patcog.2026.113973_b49","doi-asserted-by":"crossref","first-page":"5875","DOI":"10.1109\/TIP.2021.3089943","article-title":"Layercam: Exploring hierarchical class activation maps for localization","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.113973_b50","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"160","article-title":"Transformer based multiple instance learning for weakly supervised histopathology image segmentation","author":"Qian","year":"2022"},{"key":"10.1016\/j.patcog.2026.113973_b51","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"173","article-title":"Ame-cam: Attentive multiple-exit cam for weakly supervised segmentation on mri brain tumor","author":"Chen","year":"2023"},{"key":"10.1016\/j.patcog.2026.113973_b52","series-title":"2024 IEEE International Conference on Bioinformatics and Biomedicine","first-page":"3768","article-title":"AC-CAM: Affinity-aware contrast CAM for weakly-supervised semantic segmentation on MRI brain tumor","author":"Wang","year":"2024"},{"key":"10.1016\/j.patcog.2026.113973_b53","doi-asserted-by":"crossref","unstructured":"N. Araslanov, S. Roth, Single-stage semantic segmentation from image labels, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 4253\u20134262.","DOI":"10.1109\/CVPR42600.2020.00431"},{"key":"10.1016\/j.patcog.2026.113973_b54","series-title":"International MICCAI Brainlesion Workshop","first-page":"15","article-title":"Optimized U-net for brain tumor segmentation","author":"Futrega","year":"2021"},{"key":"10.1016\/j.patcog.2026.113973_b55","series-title":"The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification","author":"Baid","year":"2021"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326009386?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326009386?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T16:21:10Z","timestamp":1780935670000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326009386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":55,"alternative-id":["S0031320326009386"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113973","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing weakly supervised 3D medical image segmentation through probabilistic-aware learning","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113973","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"113973"}}