{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T02:54:48Z","timestamp":1794538488668,"version":"4.9.1"},"reference-count":88,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["T45-401\/22-N"],"award-info":[{"award-number":["T45-401\/22-N"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010428","name":"Innovation and Technology Fund","doi-asserted-by":"publisher","award":["GHP\/060\/21GD"],"award-info":[{"award-number":["GHP\/060\/21GD"]}],"id":[{"id":"10.13039\/501100010428","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010428","name":"Innovation and Technology Fund","doi-asserted-by":"publisher","award":["GHP\/252\/23SZ"],"award-info":[{"award-number":["GHP\/252\/23SZ"]}],"id":[{"id":"10.13039\/501100010428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.media.2026.103973","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:26:16Z","timestamp":1770164776000},"page":"103973","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"special_numbering":"C","title":["SAM-driven cross prompting with adaptive sampling consistency for semi-supervised medical image segmentation"],"prefix":"10.1016","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7011-1481","authenticated-orcid":false,"given":"Juzheng","family":"Miao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6040-6833","authenticated-orcid":false,"given":"Cheng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7160-9938","authenticated-orcid":false,"given":"Yuchen","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9651-5820","authenticated-orcid":false,"given":"Quanzheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3055-5034","authenticated-orcid":false,"given":"Pheng-Ann","family":"Heng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.media.2026.103973_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data Brief"},{"key":"10.1016\/j.media.2026.103973_bib0002","series-title":"Medical Image Computing and Computer-Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11\u201313, 2017, Proceedings, Part II 20","first-page":"253","article-title":"Semi-supervised learning for network-based cardiac MR image segmentation","author":"Bai","year":"2017"},{"key":"10.1016\/j.media.2026.103973_bib0003","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11514","article-title":"Bidirectional copy-paste for semi-supervised medical image segmentation","author":"Bai","year":"2023"},{"issue":"11","key":"10.1016\/j.media.2026.103973_bib0004","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","article-title":"Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?","volume":"37","author":"Bernard","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.103973_bib0005","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"9650","article-title":"Emerging properties in self-supervised vision transformers","author":"Caron","year":"2021"},{"key":"10.1016\/j.media.2026.103973_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102792","article-title":"Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation","volume":"87","author":"Chaitanya","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103310","article-title":"Ma-sam: modality-agnostic sam adaptation for 3d medical image segmentation","volume":"98","author":"Chen","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0008","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"551","article-title":"Decoupled consistency for semi-supervised medical image segmentation","author":"Chen","year":"2023"},{"key":"10.1016\/j.media.2026.103973_bib0009","series-title":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"ASLseg: adapting sam in the loop for semi-supervised liver tumor segmentation","author":"Chen","year":"2024"},{"key":"10.1016\/j.media.2026.103973_bib0010","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2613","article-title":"Semi-supervised semantic segmentation with cross pseudo supervision","author":"Chen","year":"2021"},{"key":"10.1016\/j.media.2026.103973_bib0011","unstructured":"Cheng, D., Qin, Z., Jiang, Z., Zhang, S., Lao, Q., Li, K., 2023a. Sam on medical images: a comprehensive study on three prompt modes. arXiv: 2305.00035."},{"key":"10.1016\/j.media.2026.103973_bib0012","unstructured":"Cheng, J., Ye, J., Deng, Z., Chen, J., Li, T., Wang, H., Su, Y., Huang, Z., Chen, J., Jiang, L., et al., 2023b. Sam-med2d. arXiv: 2308.16184."},{"key":"10.1016\/j.media.2026.103973_bib0013","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.media.2026.103973_bib0014","series-title":"2018 IEEE 15Th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"168","article-title":"Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)","author":"Codella","year":"2018"},{"key":"10.1016\/j.media.2026.103973_bib0015","unstructured":"Dai, H., Ma, C., Liu, Z., Li, Y., Shu, P., Wei, X., Zhao, L., Wu, Z., Zhu, D., Liu, W., et al., 2023. SAMAug: point prompt augmentation for segment anything model. arXiv: 2307.01187."},{"key":"10.1016\/j.media.2026.103973_bib0016","series-title":"IS&T International Symposium on Electronic Imaging","first-page":"COIMG","article-title":"Segment anything model (sam) for digital pathology: assess zero-shot segmentation on whole slide imaging","volume":"Vol. 37","author":"Deng","year":"2025"},{"key":"10.1016\/j.media.2026.103973_bib0017","unstructured":"DeVries, T., Taylor, G. W., 2017. Improved regularization of convolutional neural networks with cutout. arXiv: 1708.04552."},{"issue":"8","key":"10.1016\/j.media.2026.103973_bib0018","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","article-title":"Inf-Net: automatic covid-19 lung infection segmentation from ct images","volume":"39","author":"Fan","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.103973_bib0019","doi-asserted-by":"crossref","unstructured":"Gao, Y., Xia, W., Hu, D., Gao, X., 2023. DeSAM: decoupling segment anything model for generalizable medical image segmentation. arXiv: 2306.00499.","DOI":"10.1007\/978-3-031-72390-2_48"},{"key":"10.1016\/j.media.2026.103973_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103324","article-title":"3DSAM-Adapter: holistic adaptation of SAM from 2D to 3d for promptable tumor segmentation","volume":"98","author":"Gong","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0021","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"305","article-title":"FrcNet: frequency and region consistency for semi-supervised medical image segmentation","author":"He","year":"2024"},{"key":"10.1016\/j.media.2026.103973_bib0022","series-title":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"SSL-ENSAM: utilizing sam for semi-supervised retinal vessel segmentation with quality-aware enhancement","author":"He","year":"2024"},{"key":"10.1016\/j.media.2026.103973_bib0023","unstructured":"He, S., Bao, R., Li, J., Grant, P. E., Ou, Y., 2023. Accuracy of segment-anything model (sam) in medical image segmentation tasks. arXiv: 2304.09324."},{"key":"10.1016\/j.media.2026.103973_bib0024","unstructured":"Hu, C., Xia, T., Ju, S., Li, X., 2023a. When sam meets medical images: an investigation of segment anything model (sam) on multi-phase liver tumor segmentation. arXiv: 2304.08506."},{"key":"10.1016\/j.media.2026.103973_bib0025","unstructured":"Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., 2021. LoRA: low-rank adaptation of large language models. arXiv: 2106.09685."},{"key":"10.1016\/j.media.2026.103973_bib0026","series-title":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","first-page":"2040","article-title":"SESAME: toward medical image segmentation via foundation model-assisted semi-supervised learning","author":"Hu","year":"2024"},{"key":"10.1016\/j.media.2026.103973_bib0027","unstructured":"Hu, X., Xu, X., Shi, Y., 2023b. How to efficiently adapt large segmentation model (sam) to medical images. arXiv: 2306.13731."},{"key":"10.1016\/j.media.2026.103973_bib0028","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/TMI.2025.3530097","article-title":"Learnable prompting SAM-induced knowledge distillation for semi-supervised medical image segmentation","volume":"44","author":"Huang","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.103973_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.103061","article-title":"Segment anything model for medical images?","volume":"92","author":"Huang","year":"2024","journal-title":"Med. Image Anal."},{"issue":"2","key":"10.1016\/j.media.2026.103973_bib0030","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"Nnu-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.media.2026.103973_bib0031","series-title":"International Conference on Multimedia Modeling","first-page":"218","article-title":"Kvasir-instrument: diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy","author":"Jha","year":"2021"},{"key":"10.1016\/j.media.2026.103973_bib0032","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"19699","article-title":"Semi-supervised learning of semantic correspondence with pseudo-labels","author":"Kim","year":"2022"},{"key":"10.1016\/j.media.2026.103973_bib0033","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al., 2023. Segment anything. arXiv: 2304.02643.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"10.1016\/j.media.2026.103973_bib0034","doi-asserted-by":"crossref","unstructured":"Konwer, A., Yang, Z., Bas, E., Xiao, C., Prasanna, P., Bhatia, P., Kass-Hout, T., 2025. Enhancing SAM with efficient prompting and preference optimization for semi-supervised medical image segmentation. arXiv: 2503.04639.","DOI":"10.1109\/CVPR52734.2025.01955"},{"key":"10.1016\/j.media.2026.103973_bib0035","series-title":"European Conference on Computer Vision","first-page":"630","article-title":"SPSN: superpixel prototype sampling network for RGB-D salient object detection","author":"Lee","year":"2022"},{"key":"10.1016\/j.media.2026.103973_bib0036","unstructured":"Li, C., Khanduri, P., Qiang, Y., Sultan, R. I., Chetty, I., Zhu, D., 2023a. Auto-prompting sam for mobile friendly 3d medical image segmentation 2. arXiv: 2308.14936."},{"key":"10.1016\/j.media.2026.103973_bib0037","series-title":"International Conference on Neural Information Processing","first-page":"138","article-title":"Segment anything model for semi-supervised medical image segmentation via selecting reliable pseudo-labels","author":"Li","year":"2023"},{"key":"10.1016\/j.media.2026.103973_bib0038","doi-asserted-by":"crossref","first-page":"3909","DOI":"10.1109\/TMI.2025.3532084","article-title":"Stitching, fine-tuning, re-training: a SAM-enabled framework for semi-supervised 3d medical image segmentation","volume":"44","author":"Li","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.media.2026.103973_bib0039","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1109\/TNNLS.2020.2995319","article-title":"Transformation-consistent self-ensembling model for semisupervised medical image segmentation","volume":"32","author":"Li","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.media.2026.103973_bib0040","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"56","article-title":"DiffRect: latent diffusion label rectification for semi-supervised medical image segmentation","author":"Liu","year":"2024"},{"key":"10.1016\/j.media.2026.103973_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103550","article-title":"Segment any tissue: one-shot reference guided training-free automatic point prompting for medical image segmentation","volume":"102","author":"Liu","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0042","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4258","article-title":"Perturbed and strict mean teachers for semi-supervised semantic segmentation","author":"Liu","year":"2022"},{"issue":"10","key":"10.1016\/j.media.2026.103973_bib0043","doi-asserted-by":"crossref","first-page":"2832","DOI":"10.1109\/TMI.2023.3266137","article-title":"Min-max similarity: a contrastive semi-supervised deep learning network for surgical tools segmentation","volume":"42","author":"Lou","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.103973_bib0044","series-title":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","first-page":"2256","article-title":"Up-Sam: uncertainty-informed adaptation of segment anything model for semi-supervised medical image segmentation","author":"Lu","year":"2024"},{"key":"10.1016\/j.media.2026.103973_bib0045","unstructured":"Luo, X., 2020. SSL4MIS. https:\/\/github.com\/HiLab-git\/SSL4MIS."},{"key":"10.1016\/j.media.2026.103973_bib0046","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"8801","article-title":"Semi-supervised medical image segmentation through dual-task consistency","volume":"Vol. 35","author":"Luo","year":"2021"},{"key":"10.1016\/j.media.2026.103973_bib0047","series-title":"International Conference on Medical Imaging with Deep Learning","first-page":"820","article-title":"Semi-supervised medical image segmentation via cross teaching between cnn and transformer","author":"Luo","year":"2022"},{"key":"10.1016\/j.media.2026.103973_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102517","article-title":"Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency","volume":"80","author":"Luo","year":"2022","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.media.2026.103973_bib0049","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":"Nat. Commun."},{"key":"10.1016\/j.media.2026.103973_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment anything model for medical image analysis: an experimental study","volume":"89","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0051","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"167","article-title":"Cross prompting consistency with segment anything model for semi-supervised medical image segmentation","author":"Miao","year":"2024"},{"key":"10.1016\/j.media.2026.103973_bib0052","article-title":"SC-SSL: self-correcting collaborative and contrastive co-training model for semi-supervised medical image segmentation","volume":"43","author":"Miao","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.103973_bib0053","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al., 2023. DINOV2: learning robust visual features without supervision. arXiv: 2304.07193."},{"issue":"10","key":"10.1016\/j.media.2026.103973_bib0054","doi-asserted-by":"crossref","first-page":"4672","DOI":"10.1109\/JBHI.2023.3274498","article-title":"Federated semi-supervised learning for medical image segmentation via pseudo-label denoising","volume":"27","author":"Qiu","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.media.2026.103973_bib0055","series-title":"International Conference on Machine Learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.media.2026.103973_bib0056","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.media.2026.103973_bib0057","unstructured":"Shaharabany, T., Dahan, A., Giryes, R., Wolf, L., 2023. AutoSAM: adapting sam to medical images by overloading the prompt encoder. arXiv: 2306.06370."},{"key":"10.1016\/j.media.2026.103973_bib0058","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"567","article-title":"SDCL: students discrepancy-informed correction learning for semi-supervised medical image segmentation","author":"Song","year":"2024"},{"issue":"11","key":"10.1016\/j.media.2026.103973_bib0059","doi-asserted-by":"crossref","first-page":"8174","DOI":"10.1109\/TNNLS.2022.3155478","article-title":"Graph-based semi-supervised learning: a comprehensive review","volume":"34","author":"Song","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.media.2026.103973_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103111","article-title":"Mutual learning with reliable pseudo label for semi-supervised medical image segmentation","volume":"94","author":"Su","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0061","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"20730","article-title":"Self-supervised pre-training of swin transformers for 3d medical image analysis","author":"Tang","year":"2022"},{"key":"10.1016\/j.media.2026.103973_bib0062","first-page":"1195","article-title":"Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results","volume":"30","author":"Tarvainen","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2026.103973_bib0063","series-title":"Medical Imaging with Deep Learning, Short Paper Track","article-title":"SAM. MD: zero-shot medical image segmentation capabilities of the segment anything model","author":"Wald","year":"2023"},{"key":"10.1016\/j.media.2026.103973_bib0064","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.52202\/075280-0088","article-title":"Towards generic semi-supervised framework for volumetric medical image segmentation","volume":"36","author":"Wang","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2026.103973_bib0065","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"383","article-title":"Learning representations by maximizing mutual information across views for medical image segmentation","author":"Weng","year":"2024"},{"key":"10.1016\/j.media.2026.103973_bib0066","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103547","article-title":"Medical Sam adapter: adapting segment anything model for medical image segmentation","volume":"102","author":"Wu","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0067","article-title":"Mutual consistency learning for semi-supervised medical image segmentation","volume":"81","author":"Wu","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0068","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101766","article-title":"Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation","volume":"65","author":"Xia","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0069","unstructured":"Xie, B., Tang, H., Cai, D., Yan, Y., Agam, G., 2025. Self-prompt SAM: medical image segmentation via automatic prompt SAM adaptation. arXiv: 2502.00630."},{"key":"10.1016\/j.media.2026.103973_bib0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101832","article-title":"A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging","volume":"67","author":"Xiong","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0071","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"9112","article-title":"SGTC: semantic-guided triplet co-training for sparsely annotated semi-supervised medical image segmentation","volume":"Vol. 39","author":"Yan","year":"2025"},{"key":"10.1016\/j.media.2026.103973_bib0072","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"3","article-title":"Searching learning strategy with reinforcement learning for 3d medical image segmentation","author":"Yang","year":"2019"},{"key":"10.1016\/j.media.2026.103973_bib0073","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"7236","article-title":"Revisiting weak-to-strong consistency in semi-supervised semantic segmentation","author":"Yang","year":"2023"},{"key":"10.1016\/j.media.2026.103973_bib0074","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2025.3528453","article-title":"UniMatch V2: pushing the limit of semi-supervised semantic segmentation","volume":"47","author":"Yang","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2026.103973_bib0075","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"3099","article-title":"Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation","volume":"Vol. 36","author":"Yao","year":"2022"},{"key":"10.1016\/j.media.2026.103973_bib0076","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22","first-page":"605","article-title":"Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation","author":"Yu","year":"2019"},{"key":"10.1016\/j.media.2026.103973_bib0077","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"6890","article-title":"SurgicalSAM: efficient class promptable surgical instrument segmentation","volume":"Vol. 38","author":"Yue","year":"2024"},{"key":"10.1016\/j.media.2026.103973_bib0078","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"6023","article-title":"CutMix: regularization strategy to train strong classifiers with localizable features","author":"Yun","year":"2019"},{"issue":"8","key":"10.1016\/j.media.2026.103973_bib0079","doi-asserted-by":"crossref","first-page":"10427","DOI":"10.1109\/TPAMI.2023.3240886","article-title":"Ss-TBN: a semi-supervised tri-branch network for covid-19 screening and lesion segmentation","volume":"45","author":"Zeng","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2026.103973_bib0080","first-page":"18408","article-title":"FlexMatch: boosting semi-supervised learning with curriculum pseudo labeling","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2026.103973_bib0081","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liu, D., 2023. Customized segment anything model for medical image segmentation. arXiv: 2304.13785.","DOI":"10.2139\/ssrn.4495221"},{"key":"10.1016\/j.media.2026.103973_bib0082","unstructured":"Zhang, Y., Cheng, Y., Qi, Y., 2023. SemiSAM: exploring SAM for enhancing semi-supervised medical image segmentation with extremely limited annotations. arXiv: 2312.06316."},{"key":"10.1016\/j.media.2026.103973_bib0083","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103733","article-title":"SemiSAM+: rethinking semi-supervised medical image segmentation in the era of foundation models","volume":"106","author":"Zhang","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103973_bib0084","series-title":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","first-page":"343","article-title":"Combining segment anything model with domain-specific knowledge for semi-supervised learning in medical image segmentation","author":"Zhang","year":"2024"},{"key":"10.1016\/j.media.2026.103973_bib0085","doi-asserted-by":"crossref","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","article-title":"NnFormer: volumetric medical image segmentation via a 3d transformer","volume":"32","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Image Process."},{"issue":"10","key":"10.1016\/j.media.2026.103973_bib0086","doi-asserted-by":"crossref","first-page":"6858","DOI":"10.1109\/TPAMI.2024.3387116","article-title":"Prototype-based semantic segmentation","volume":"46","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2026.103973_bib0087","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2582","article-title":"Rethinking semantic segmentation: a prototype view","author":"Zhou","year":"2022"},{"key":"10.1016\/j.media.2026.103973_bib0088","unstructured":"Zhou, T., Zhang, Y., Zhou, Y., Wu, Y., Gong, C., 2023b. Can sam segment polyps?arXiv: 2304.07583."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526000423?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526000423?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T11:09:05Z","timestamp":1783422545000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841526000423"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":88,"alternative-id":["S1361841526000423"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2026.103973","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SAM-driven cross prompting with adaptive sampling consistency for semi-supervised medical image segmentation","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2026.103973","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 B.V.","name":"copyright","label":"Copyright"}],"article-number":"103973"}}