{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T15:10:05Z","timestamp":1781622605592,"version":"3.54.5"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049360","type":"print"},{"value":"9783032049377","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04937-7_55","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:41:07Z","timestamp":1758260467000},"page":"579-588","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SUGFW: A SAM-Based Uncertainty-Guided Feature Weighting Framework for\u00a0Cold Start Active Learning"],"prefix":"10.1007","author":[{"given":"Xiaochuan","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lanfeng","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guotai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"55_CR1","doi-asserted-by":"crossref","unstructured":"Bengar, J.Z., van\u00a0de Weijer, J., Twardowski, B., Raducanu, B.: Reducing label effort: self-supervised meets active learning. In: ICCV, pp. 1631\u20131639 (2021)","DOI":"10.1109\/ICCVW54120.2021.00188"},{"key":"55_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102062","volume":"71","author":"S Budd","year":"2021","unstructured":"Budd, S., Robinson, E.C., Kainz, B.: A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med. Image Anal. 71, 102062 (2021)","journal-title":"Med. Image Anal."},{"key":"55_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103310","volume":"98","author":"C Chen","year":"2024","unstructured":"Chen, C., et al.: Ma-sam: modality-agnostic sam adaptation for 3D medical image segmentation. Med. Image Anal. 98, 103310 (2024)","journal-title":"Med. Image Anal."},{"key":"55_CR4","unstructured":"Chen, L., et al.: Making your first choice: to address cold start problem in medical active learning. In: Medical Imaging with Deep Learning, pp. 496\u2013525. PMLR (2024)"},{"key":"55_CR5","doi-asserted-by":"crossref","unstructured":"Deng, G., et al.: Sam-u: multi-box prompts triggered uncertainty estimation for reliable sam in medical image. In: MICCAI, pp. 368\u2013377 (2023)","DOI":"10.1007\/978-3-031-47425-5_33"},{"key":"55_CR6","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2020)"},{"key":"55_CR7","unstructured":"Hacohen, G., Dekel, A., Weinshall, D.: Active learning on a budget: opposite strategies suit high and low budgets. In: ICML, pp. 8175\u20138195. PMLR (2022)"},{"key":"55_CR8","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.ins.2022.10.066","volume":"616","author":"Q Jin","year":"2022","unstructured":"Jin, Q., Yuan, M., Li, S., Wang, H., Wang, M., Song, Z.: Cold-start active learning for image classification. Inf. Sci. 616, 16\u201336 (2022)","journal-title":"Inf. Sci."},{"key":"55_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108278","volume":"241","author":"Q Jin","year":"2022","unstructured":"Jin, Q., Yuan, M., Qiao, Q., Song, Z.: One-shot active learning for image segmentation via contrastive learning and diversity-based sampling. Knowl.-Based Syst. 241, 108278 (2022)","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"55_CR10","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.1007\/s10462-022-10245-x","volume":"56","author":"P Jyothi","year":"2023","unstructured":"Jyothi, P., Singh, A.R.: Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review. Artif. Intell. Rev. 56(4), 2923\u20132969 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"55_CR11","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. In: ICCV, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"2","key":"55_CR12","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.media.2013.12.002","volume":"18","author":"G Litjens","year":"2014","unstructured":"Litjens, G., et al.: Evaluation of prostate segmentation algorithms for MRI: the promise12 challenge. Med. Image Anal. 18(2), 359\u2013373 (2014)","journal-title":"Med. Image Anal."},{"key":"55_CR13","doi-asserted-by":"publisher","unstructured":"Liu, H., et al.: Colossal: a benchmark for cold-start active learning for 3D medical image segmentation. In: MICCAI, pp. 25\u201334. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-43895-0_3","DOI":"10.1007\/978-3-031-43895-0_3"},{"key":"55_CR14","doi-asserted-by":"publisher","unstructured":"Liu, Y., Li, W., Wang, C., Chen, H., Yuan, Y.: When 3D partial points meets sam: tooth point cloud segmentation with sparse labels. In: MICCAI, pp. 778\u2013788. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-72120-5_72","DOI":"10.1007\/978-3-031-72120-5_72"},{"key":"55_CR15","doi-asserted-by":"publisher","unstructured":"Luo, Z., Luo, X., Gao, Z., Wang, G.: An uncertainty-guided tiered self-training framework for active source-free domain adaptation in prostate segmentation. In: MICCAI, pp. 107\u2013117. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-72114-4_11","DOI":"10.1007\/978-3-031-72114-4_11"},{"key":"55_CR16","doi-asserted-by":"crossref","unstructured":"Neupane, K.P., Zheng, E., Yu, Q.: Metaedl: meta evidential learning for uncertainty-aware cold-start recommendations. In: ICDM, pp. 1258\u20131263. IEEE (2021)","DOI":"10.1109\/ICDM51629.2021.00154"},{"key":"55_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"55_CR18","unstructured":"Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. STAT 1050, 21 (2018)"},{"key":"55_CR19","doi-asserted-by":"publisher","first-page":"56","DOI":"10.3389\/fncom.2019.00056","volume":"13","author":"G Wang","year":"2019","unstructured":"Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation. Front. Comput. Neurosci. 13, 56 (2019)","journal-title":"Front. Comput. Neurosci."},{"key":"55_CR20","doi-asserted-by":"crossref","unstructured":"Wang, H., Jin, Q., Li, S., Liu, S., Wang, M., Song, Z.: A comprehensive survey on deep active learning in medical image analysis. Med. Image Anal. 103201 (2024)","DOI":"10.1016\/j.media.2024.103201"},{"key":"55_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/978-3-030-59710-8_4","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"J Wang","year":"2020","unstructured":"Wang, J., Yan, Y., Zhang, Y., Cao, G., Yang, M., Ng, M.K.: Deep reinforcement active learning for medical image classification. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 33\u201342. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_4"},{"key":"55_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101832","volume":"67","author":"Z Xiong","year":"2021","unstructured":"Xiong, Z., et al.: A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med. Image Anal. 67, 101832 (2021)","journal-title":"Med. Image Anal."},{"key":"55_CR23","doi-asserted-by":"crossref","unstructured":"Xu, W., Hu, Z., Lu, Y., Meng, J., Liu, Q., Wang, Y.: Activedc: distribution calibration for active finetuning. In: CVPR, pp. 16996\u201317005 (2024)","DOI":"10.1109\/CVPR52733.2024.01608"},{"key":"55_CR24","first-page":"22354","volume":"35","author":"O Yehuda","year":"2022","unstructured":"Yehuda, O., Dekel, A., Hacohen, G., Weinshall, D.: Active learning through a covering lens. NeurIPS 35, 22354\u201322367 (2022)","journal-title":"NeurIPS"},{"key":"55_CR25","doi-asserted-by":"crossref","unstructured":"Yoo, D., Kweon, I.S.: Learning loss for active learning. In: CVPR, pp. 93\u2013102 (2019)","DOI":"10.1109\/CVPR.2019.00018"},{"key":"55_CR26","doi-asserted-by":"crossref","unstructured":"Yuan, M., Lin, H.T., Boyd-Graber, J.: Cold-start active learning through self-supervised language modeling. In: EMNLP, pp. 7935\u20137948 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.637"},{"key":"55_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Lu, W., Zeng, Z., Xu, K., Veeravalli, B., Guan, C.: Self-supervised assisted active learning for skin lesion segmentation. In: EMBC, pp. 5043\u20135046. IEEE (2022)","DOI":"10.1109\/EMBC48229.2022.9871734"},{"key":"55_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103542","volume":"102","author":"L Zhong","year":"2025","unstructured":"Zhong, L., et al.: Unisal: unified semi-supervised active learning for histopathological image classification. Med. Image Anal. 102, 103542 (2025)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04937-7_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:41:17Z","timestamp":1758260477000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04937-7_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049360","9783032049377"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04937-7_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that\u00a0are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}