{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:45:27Z","timestamp":1743108327004,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031438943"},{"type":"electronic","value":"9783031438950"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43895-0_2","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:23Z","timestamp":1696115303000},"page":"14-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SLPT: Selective Labeling Meets Prompt Tuning on\u00a0Label-Limited Lesion Segmentation"],"prefix":"10.1007","author":[{"given":"Fan","family":"Bai","sequence":"first","affiliation":[]},{"given":"Ke","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Xiaoyu","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Xinyu","family":"Mao","sequence":"additional","affiliation":[]},{"given":"Xiaoli","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Jingren","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Le","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Max Q.-H.","family":"Meng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680","volume":"84","author":"P Bilic","year":"2023","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). Med. Image Anal. 84, 102680 (2023)","journal-title":"Med. Image Anal."},{"key":"2_CR2","unstructured":"Allingham, J.U., et al.: A simple zero-shot prompt weighting technique to improve prompt ensembling in text-image models. arXiv preprint arXiv:2302.06235 (2023)"},{"key":"2_CR3","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/978-3-031-16452-1_3","volume-title":"MICCAI 2022","author":"F Bai","year":"2022","unstructured":"Bai, F., Xing, X., Shen, Y., Ma, H., Meng, M.Q.H.: Discrepancy-based active learning for weakly supervised bleeding segmentation in wireless capsule endoscopy images. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13438, pp. 24\u201334. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_3"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Beluch, W.H., Genewein, T., N\u00fcrnberger, A., K\u00f6hler, J.M.: The power of ensembles for active learning in image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9368\u20139377 (2018)","DOI":"10.1109\/CVPR.2018.00976"},{"key":"2_CR5","unstructured":"Brown, T.B., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, pp. 1876\u20131901 (2020)"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Caramalau, R., Bhattarai, B., Kim, T.K.: Sequential graph convolutional network for active learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9583\u20139592 (2021)","DOI":"10.1109\/CVPR46437.2021.00946"},{"key":"2_CR7","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","volume":"54","author":"V Cheplygina","year":"2019","unstructured":"Cheplygina, V., de Bruijne, M., Pluim, J.P.: Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med. Image Anal. 54, 280\u2013296 (2019)","journal-title":"Med. Image Anal."},{"key":"2_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1007\/978-3-030-59719-1_16","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"C Dai","year":"2020","unstructured":"Dai, C., et al.: Suggestive annotation of brain tumour images with gradient-guided sampling. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 156\u2013165. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_16"},{"key":"2_CR10","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059. PMLR (2016)"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"2","key":"2_CR12","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"2_CR13","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1007\/978-3-031-19827-4_41","volume-title":"ECCV 2022","author":"M Jia","year":"2022","unstructured":"Jia, M., et al.: Visual prompt tuning. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13693, pp. 709\u2013727. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19827-4_41"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Kim, M., et al.: Deep learning in medical imaging. Neurospine 16(4), 657 (2019)","DOI":"10.14245\/ns.1938396.198"},{"issue":"1","key":"2_CR15","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79\u201386 (1951)","journal-title":"Ann. Math. Stat."},{"key":"2_CR16","unstructured":"Kumar, A., Raghunathan, A., Jones, R., Ma, T., Liang, P.: Fine-tuning can distort pretrained features and underperform out-of-distribution. arXiv preprint arXiv:2202.10054 (2022)"},{"key":"2_CR17","unstructured":"Liu, L., Yu, B.X., Chang, J., Tian, Q., Chen, C.W.: Prompt-matched semantic segmentation. arXiv preprint arXiv:2208.10159 (2022)"},{"issue":"9","key":"2_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560815","volume":"55","author":"P Liu","year":"2023","unstructured":"Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55(9), 1\u201335 (2023)","journal-title":"ACM Comput. Surv."},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Parvaneh, A., Abbasnejad, E., Teney, D., Haffari, G.R., Van Den Hengel, A., Shi, J.Q.: Active learning by feature mixing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12237\u201312246 (2022)","DOI":"10.1109\/CVPR52688.2022.01192"},{"key":"2_CR20","unstructured":"Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061 (2020)"},{"key":"2_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-319-67389-9_44","volume-title":"Machine Learning in Medical Imaging","author":"SSM Salehi","year":"2017","unstructured":"Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 379\u2013387. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67389-9_44"},{"key":"2_CR22","unstructured":"Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. arXiv preprint arXiv:1708.00489 (2017)"},{"key":"2_CR23","unstructured":"Settles, B.: Active learning literature survey (2009)"},{"key":"2_CR24","unstructured":"Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)"},{"key":"2_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101693","volume":"63","author":"N Tajbakhsh","year":"2020","unstructured":"Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J.N., Wu, Z., Ding, X.: Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med. Image Anal. 63, 101693 (2020)","journal-title":"Med. Image Anal."},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Wang, T., et al.: Boosting active learning via improving test performance. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 8566\u20138574 (2022)","DOI":"10.1609\/aaai.v36i8.20834"},{"key":"2_CR27","unstructured":"Zhan, X., Wang, Q., Huang, K.H., Xiong, H., Dou, D., Chan, A.B.: A comparative survey of deep active learning. arXiv preprint arXiv:2203.13450 (2022)"},{"key":"2_CR28","unstructured":"Zhao, T., et al.: Prompt design for text classification with transformer-based models. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2709\u20132722 (2021)"},{"issue":"9","key":"2_CR29","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. Int. J. Comput. Vision 130(9), 2337\u20132348 (2022)","journal-title":"Int. J. Comput. Vision"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43895-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:28:49Z","timestamp":1710167329000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43895-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438943","9783031438950"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43895-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"730","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}