{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:14:41Z","timestamp":1775578481750,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031477232","type":"print"},{"value":"9783031477249","type":"electronic"}],"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\/978-3-031-47724-9_40","type":"book-chapter","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T20:29:08Z","timestamp":1713472148000},"page":"610-628","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Q-Net: Query-Informed Few-Shot Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Qianqian","family":"Shen","sequence":"first","affiliation":[]},{"given":"Yanan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jiyong","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 424\u2013432. Springer (2016)","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"40_CR3","unstructured":"Dong, N., Xing, E.P.: Few-shot semantic segmentation with prototype learning. In: BMVC, pp. 1\u201313 (2018)"},{"key":"40_CR4","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":"40_CR5","doi-asserted-by":"crossref","unstructured":"Hesamian, M.H., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32(4), 582\u2013596 (2019)","DOI":"10.1007\/s10278-019-00227-x"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P.F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., Wirkert, S., et\u00a0al.: nnu-net: self-adapting framework for u-net-based medical image segmentation (2018). arxiv:1809.10486","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"40_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101950","volume":"69","author":"A Kavur","year":"2021","unstructured":"Kavur, A., Gezer, N., Bar\u0131\u015f, M., Aslan, S., Conze, P., Groza, V., Pham, D., Chatterjee, S., Ernst, P., \u00d6zkan, S.: CHAOs challenge-combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. 69, 101950 (2021)","journal-title":"Med. Image Anal."},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, X., Zhang, S., He, X.: Part-aware prototype network for few-shot semantic segmentation. In: European Conference on Computer Vision, pp. 142\u2013158. Springer (2020)","DOI":"10.1007\/978-3-030-58545-7_9"},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"40_CR10","doi-asserted-by":"crossref","unstructured":"Mehta, S., Mercan, E., Bartlett, J., Weaver, D., Elmore, J.G., Shapiro, L.: Y-net: joint segmentation and classification for diagnosis of breast biopsy images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 893\u2013901. Springer (2018)","DOI":"10.1007\/978-3-030-00934-2_99"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520\u20131528 (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"40_CR13","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et\u00a0al.: Attention u-net: learning where to look for the pancreas (2018). arxiv:1804.03999"},{"key":"40_CR14","unstructured":"Oreshkin, B., L\u00f3pez, P.R., Lacoste, A.: Tadam: task dependent adaptive metric for improved few-shot learning. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"\u00d8rting, S., Doyle, A., van Hilten, A., Hirth, M., Inel, O., Madan, C.R., Mavridis, P., Spiers, H., Cheplygina, V.: A survey of crowdsourcing in medical image analysis (2019). arxiv:1902.09159","DOI":"10.15346\/hc.v7i1.111"},{"key":"40_CR16","doi-asserted-by":"crossref","unstructured":"Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with superpixels: training few-shot medical image segmentation without annotation. In: European Conference on Computer Vision, pp. 762\u2013780. Springer (2020)","DOI":"10.1007\/978-3-030-58526-6_45"},{"key":"40_CR17","unstructured":"Rakelly, K., Shelhamer, E., Darrell, T., Efros, A.A., Levine, S.: Few-shot segmentation propagation with guided networks (2018). arxiv:1806.07373"},{"key":"40_CR18","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Roy, A.G., Siddiqui, S., P\u00f6lsterl, S., Navab, N., Wachinger, C.: Squeeze & Excite\u2019guided few-shot segmentation of volumetric images. Med. Image Anal. 59, 101587 (2020)","DOI":"10.1016\/j.media.2019.101587"},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Shaban, A., Bansal, S., Liu, Z., Essa, I., Boots, B.: One-shot learning for semantic segmentation (2017). arxiv:1709.03410","DOI":"10.5244\/C.31.167"},{"key":"40_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105067","volume":"140","author":"L Sun","year":"2022","unstructured":"Sun, L., Li, C., Ding, X., Huang, Y., Chen, Z., Wang, G., Yu, Y., Paisley, J.: Few-shot medical image segmentation using a global correlation network with discriminative embedding. Comput. Biol. Med. 140, 105067 (2022)","journal-title":"Comput. Biol. Med."},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, X., Sun, S., Yan, X., Xie, X.: Recurrent mask refinement for few-shot medical image segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3918\u20133928 (2021)","DOI":"10.1109\/ICCV48922.2021.00389"},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"Wang, G., Zuluaga, M.A., Li, W., Pratt, R., Patel, P.A., Aertsen, M., Doel, T., David, A.L., Deprest, J., Ourselin, S., et\u00a0al.: DeepIGeoS: a deep interactive geodesic framework for medical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1559\u20131572 (2018)","DOI":"10.1109\/TPAMI.2018.2840695"},{"key":"40_CR24","doi-asserted-by":"crossref","unstructured":"Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: PANet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9197\u20139206 (2019)","DOI":"10.1109\/ICCV.2019.00929"},{"key":"40_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.clinimag.2020.10.014","volume":"70","author":"W Wang","year":"2021","unstructured":"Wang, W., Wang, G., Xiaofen, W., Ding, X., Cao, X., Wang, L., Zhang, J., Wang, P.: Automatic segmentation of prostate magnetic resonance imaging using generative adversarial networks. Clin. Imaging 70, 1\u20139 (2021)","journal-title":"Clin. Imaging"},{"key":"40_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wei, Y., Yang, Y., Huang, T.: SG-One: similarity guidance network for one-shot semantic segmentation. IEEE Trans. Cybern. 50(9), 3855\u20133865 (2020)","DOI":"10.1109\/TCYB.2020.2992433"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Zhuang, X.: Multivariate mixture model for cardiac segmentation from multi-sequence MRI. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 581\u2013588. Springer (2016)","DOI":"10.1007\/978-3-319-46723-8_67"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47724-9_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T20:40:29Z","timestamp":1713472829000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47724-9_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031477232","9783031477249"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47724-9_40","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"19 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}