{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:19:59Z","timestamp":1743128399114,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031721137"},{"type":"electronic","value":"9783031721144"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72114-4_31","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:01:43Z","timestamp":1727874103000},"page":"318-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hemodynamic-Driven Multi-prototypes Learning for\u00a0One-Shot Segmentation in\u00a0Breast Cancer DCE-MRI"],"prefix":"10.1007","author":[{"given":"Xiang","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyun","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianxu","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"31_CR1","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"31_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102834","volume":"87","author":"Y Feng","year":"2023","unstructured":"Feng, Y., Wang, Y., Li, H., Qu, M., Yang, J.: Learning what and where to segment: a new perspective on medical image few-shot segmentation. Med. Image Anal. 87, 102834 (2023)","journal-title":"Med. Image Anal."},{"issue":"4","key":"31_CR4","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1093\/jnci\/djr545","volume":"104","author":"B Haibe-Kains","year":"2012","unstructured":"Haibe-Kains, B., et al.: A three-gene model to robustly identify breast cancer molecular subtypes. J. Natl. Cancer Inst. 104(4), 311\u2013325 (2012)","journal-title":"J. Natl. Cancer Inst."},{"key":"31_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102385","volume":"78","author":"S Hansen","year":"2022","unstructured":"Hansen, S., Gautam, S., Jenssen, R., Kampffmeyer, M.: Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels. Med. Image Anal. 78, 102385 (2022)","journal-title":"Med. Image Anal."},{"key":"31_CR6","unstructured":"Irving, B.: maskSLIC: regional superpixel generation with application to local pathology characterisation in medical images. arXiv preprint arXiv:1606.09518 (2016)"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Jampani, V., Sun, D., Liu, M.Y., Yang, M.H., Kautz, J.: Superpixel sampling networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 352\u2013368 (2018)","DOI":"10.1007\/978-3-030-01234-2_22"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Kim, S., An, S., Chikontwe, P., Park, S.H.: Bidirectional RNN-based few shot learning for 3D medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 1808\u20131816 (2021). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16275","DOI":"10.1609\/aaai.v35i3.16275"},{"issue":"9","key":"31_CR9","doi-asserted-by":"publisher","first-page":"10669","DOI":"10.1109\/TPAMI.2023.3265865","volume":"45","author":"C Lang","year":"2023","unstructured":"Lang, C., Cheng, G., Tu, B., Li, C., Han, J.: Base and meta: a new perspective on few-shot segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(9), 10669\u201310686 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Lei, W., et al.: One-shot weakly-supervised segmentation in 3D medical images. IEEE Trans. Med. Imaging (2023)","DOI":"10.1109\/TMI.2023.3294975"},{"key":"31_CR11","doi-asserted-by":"publisher","first-page":"16012","DOI":"10.1038\/npjbcancer.2016.12","volume":"2","author":"H Li","year":"2016","unstructured":"Li, H., et al.: Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA\/TCIA data set. NPJ Breast Cancer 2, 16012 (2016)","journal-title":"NPJ Breast Cancer"},{"key":"31_CR12","unstructured":"Lingle, W., et\u00a0al.: The cancer genome atlas breast invasive carcinoma collection (TCGA-BRCA)(version 3)[data set]. Cancer Imag. Arch. (2016)"},{"issue":"12","key":"31_CR13","doi-asserted-by":"publisher","first-page":"3140","DOI":"10.1007\/s11263-022-01677-7","volume":"130","author":"W Liu","year":"2022","unstructured":"Liu, W., Zhang, C., Lin, G., Liu, F.: CRCNet: few-shot segmentation with cross-reference and region-global conditional networks. Int. J. Comput. Vision 130(12), 3140\u20133157 (2022)","journal-title":"Int. J. Comput. Vision"},{"issue":"10","key":"31_CR14","doi-asserted-by":"publisher","first-page":"2672","DOI":"10.1109\/TMI.2020.3043375","volume":"40","author":"Y Lu","year":"2020","unstructured":"Lu, Y., et al.: Contour transformer network for one-shot segmentation of anatomical structures. IEEE Trans. Med. Imaging 40(10), 2672\u20132684 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"31_CR15","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-031-43901-8_10","volume-title":"MICCAI 2023","author":"T Lv","year":"2023","unstructured":"Lv, T., Liu, Y., Miao, K., Li, L., Pan, X.: Diffusion kinetic model for breast cancer segmentation in incomplete DCE-MRI. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14223, pp. 100\u2013109. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43901-8_10"},{"key":"31_CR16","unstructured":"Newitt, D., Hylton, N.: Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy. Cancer Imaging Arch. 2 (2016)"},{"key":"31_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"},{"issue":"2","key":"31_CR18","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1109\/TPAMI.2020.3013717","volume":"44","author":"Z Tian","year":"2020","unstructured":"Tian, Z., Zhao, H., Shu, M., Yang, Z., Li, R., Jia, J.: Prior guided feature enrichment network for few-shot segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 1050\u20131065 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR19","unstructured":"Wu, J., et al.: Medical SAM adapter: adapting segment anything model for medical image segmentation. arXiv preprint arXiv:2304.12620 (2023)"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lin, G., Liu, F., Yao, R., Shen, C.: Canet: class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5217\u20135226 (2019)","DOI":"10.1109\/CVPR.2019.00536"},{"key":"31_CR21","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/978-3-031-43901-8_54","volume-title":"MICCAI 2023","author":"Y Zhong","year":"2023","unstructured":"Zhong, Y., Wang, Y.: Simple: similarity-aware propagation learning for weakly-supervised breast cancer segmentation in DCE-MRI. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14223, pp. 567\u2013577. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43901-8_54"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72114-4_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:05:04Z","timestamp":1727874304000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72114-4_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721137","9783031721144"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72114-4_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are 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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}