{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T06:28:56Z","timestamp":1768112936073,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819544448","type":"print"},{"value":"9789819544455","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-4445-5_37","type":"book-chapter","created":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T03:44:23Z","timestamp":1768103063000},"page":"547-561","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Entropy-Guided Distillation for\u00a0Medical Image Segmentation Under Missing Modalities"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3201-1885","authenticated-orcid":false,"given":"Jinming","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2824-1431","authenticated-orcid":false,"given":"Yuyao","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8600-2570","authenticated-orcid":false,"given":"Xi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3034-9639","authenticated-orcid":false,"given":"Kaizhu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.media.2016.07.009","volume":"35","author":"O Maier","year":"2017","unstructured":"Maier, O.: ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250\u2013269 (2017). https:\/\/doi.org\/10.1016\/j.media.2016.07.009","journal-title":"Med. Image Anal."},{"issue":"10","key":"37_CR2","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H.: The Multimodal Brain Tumor Image Segmentation Benchmark. IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015). https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans. Med. Imaging"},{"key":"37_CR3","doi-asserted-by":"publisher","unstructured":"Lee, D., Kim, J., Moon, W.J., Ye, J.C.: CollaGAN: Collaborative GAN for missing image data imputation. In: Proceedings of the 32nd IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2482\u20132491. IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00259","DOI":"10.1109\/CVPR.2019.00259"},{"key":"37_CR4","unstructured":"Su, Z., et al.: Navigating distribution shifts in medical image analysis: a survey. arXiv preprint arXiv:2411.05824 (2024). https:\/\/arxiv.org\/abs\/2411.05824"},{"key":"37_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-68127-6_1","volume-title":"Simulation and Synthesis in Medical Imaging","author":"A Chartsias","year":"2017","unstructured":"Chartsias, A., Joyce, T., Dharmakumar, R., Tsaftaris, S.A.: Adversarial image synthesis for unpaired multi-modal cardiac data. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2017. LNCS, vol. 10557, pp. 3\u201313. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68127-6_1"},{"key":"37_CR6","doi-asserted-by":"publisher","unstructured":"Huo, Y., et al.: Adversarial synthesis learning enables segmentation without target modality ground truth. In: Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, pp. 1217\u20131220. IEEE (2018). https:\/\/doi.org\/10.1109\/ISBI.2018.8363835","DOI":"10.1109\/ISBI.2018.8363835"},{"key":"37_CR7","doi-asserted-by":"publisher","first-page":"4263","DOI":"10.1109\/TIP.2021.3070752","volume":"30","author":"T Zhou","year":"2021","unstructured":"Zhou, T., Canu, S., Vera, P., Ruan, S.: Latent correlation representation learning for brain tumor segmentation with missing MRI modalities. IEEE Trans. Image Process. 30, 4263\u20134274 (2021). https:\/\/doi.org\/10.1109\/TIP.2021.3070752","journal-title":"IEEE Trans. Image Process."},{"key":"37_CR8","doi-asserted-by":"publisher","unstructured":"Dorent, R., Joutard, S., Modat, M., Ourselin, S., Vercauteren, T.: Hetero-modal variational encoder-decoder for joint modality completion and segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 74\u201382. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_9","DOI":"10.1007\/978-3-030-32245-8_9"},{"key":"37_CR9","doi-asserted-by":"publisher","unstructured":"Chen, C., et al.: Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 447\u2013456. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_50","DOI":"10.1007\/978-3-030-32248-9_50"},{"key":"37_CR10","doi-asserted-by":"publisher","unstructured":"Zhang, Y., et al.: mmFormer: multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022. Lecture Notes in Computer Science, vol. 13435, pp. 107\u2013117. Springer, Cham (2022).https:\/\/doi.org\/10.1007\/978-3-031-16443-9_11","DOI":"10.1007\/978-3-031-16443-9_11"},{"key":"37_CR11","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the Knowledge in a Neural Network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"37_CR12","doi-asserted-by":"publisher","unstructured":"Wang, S., et al.: Prototype knowledge distillation for medical segmentation with missing modality. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1\u20135. IEEE, New York (2023). https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10095014","DOI":"10.1109\/ICASSP49357.2023.10095014"},{"key":"37_CR13","unstructured":"Lopez-Paz, D., Bottou, L., Sch\u00f6lkopf, B., Vapnik, V.: Unifying Distillation and Privileged Information. arXiv preprint arXiv:1511.03643 (2015)"},{"key":"37_CR14","doi-asserted-by":"publisher","unstructured":"Hu, M., et al.: Knowledge Distillation from Multi-modal to Mono-modal Segmentation Networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 772\u2013781. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_75","DOI":"10.1007\/978-3-030-59710-8_75"},{"issue":"3","key":"37_CR15","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1109\/TMI.2021.3122265","volume":"41","author":"C Chen","year":"2022","unstructured":"Chen, C., Dou, Q., Jin, Y., Liu, Q., Heng, P.A.: Learning with privileged multimodal knowledge for unimodal segmentation. IEEE Trans. Med. Imaging 41(3), 621\u2013632 (2022). https:\/\/doi.org\/10.1109\/TMI.2021.3122265","journal-title":"IEEE Trans. Med. Imaging"},{"key":"37_CR16","unstructured":"Romero, A., et al.: FitNets: hints for thin deep nets. In: Proceedings of the International Conference on Learning Representations (2015). arXiv:1412.6550"},{"key":"37_CR17","unstructured":"Foret, P., Kleiner, A., Mobahi, H., Neyshabur, B.: Sharpness-Aware Minimization for Efficiently Improving Generalization. In: Proceedings of the International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=6Tm1mposlrM"},{"key":"37_CR18","doi-asserted-by":"publisher","unstructured":"Liu, Y., et al.: Structured Knowledge Distillation for Semantic Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2604\u20132613 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00271","DOI":"10.1109\/CVPR.2019.00271"},{"key":"37_CR19","doi-asserted-by":"publisher","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vis.129(6), 1789\u20131819 (2021). https:\/\/doi.org\/10.1007\/s11263-021-01453-z","DOI":"10.1007\/s11263-021-01453-z"},{"key":"37_CR20","unstructured":"Kwon, J., Kim, J., Park, H., Choi, I.K.: ASAM: adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks. In: Proceedings of the 38th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 139, pp. 5905\u20135914 (2021). https:\/\/proceedings.mlr.press\/v139\/kwon21b.html"},{"key":"37_CR21","unstructured":"Zhuang, J., et al.: Surrogate gap minimization improves sharpness-aware training. In: Proceedings of the 10th International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=edONMAnhLu-"},{"key":"37_CR22","unstructured":"Liu, T., et al.: Medmap: promoting incomplete multi-modal brain tumor segmentation with alignment. arXiv preprint arXiv:2408.09465 (2024). https:\/\/arxiv.org\/abs\/2408.09465"},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Liu, T., Jiang, H., Huang, K.: KMD: Koopman multi-modality decomposition for generalized brain tumor segmentation under incomplete modalities. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15663\u201315671 (2025)","DOI":"10.1109\/CVPR52734.2025.01460"},{"key":"37_CR24","doi-asserted-by":"publisher","unstructured":"Liu, T., Wang, J., Jiang, H., Huang, K.: Leveraging graph neural networks in transferring multimodal knowledge for unimodal segmentation. In: 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), pp. 1\u20134 (2025). https:\/\/doi.org\/10.1109\/ISBI60581.2025.10981298","DOI":"10.1109\/ISBI60581.2025.10981298"},{"issue":"7","key":"37_CR25","doi-asserted-by":"publisher","first-page":"3396","DOI":"10.1109\/JBHI.2023.3270434","volume":"27","author":"Z Su","year":"2023","unstructured":"Su, Z.: Mind the gap: alleviating local imbalance for unsupervised cross-modality medical image segmentation. IEEE J. Biomed. Health Inform. 27(7), 3396\u20133407 (2023). https:\/\/doi.org\/10.1109\/JBHI.2023.3270434","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"37_CR26","doi-asserted-by":"publisher","unstructured":"Su, Z., et al.: Rethinking data augmentation for single-source domain generalization in medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 2366\u20132374 (2023). https:\/\/doi.org\/10.1609\/aaai.v37i2.25332","DOI":"10.1609\/aaai.v37i2.25332"},{"issue":"10","key":"37_CR27","doi-asserted-by":"publisher","first-page":"4976","DOI":"10.1109\/JBHI.2022.3162118","volume":"26","author":"K Yao","year":"2022","unstructured":"Yao, K.: A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation. IEEE J. Biomed. Health Inform. 26(10), 4976\u20134986 (2022). https:\/\/doi.org\/10.1109\/JBHI.2022.3162118","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4445-5_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T03:44:25Z","timestamp":1768103065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4445-5_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819544448","9789819544455"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4445-5_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Okinawa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"20 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2025.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}