{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T13:44:09Z","timestamp":1758807849621,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031721199"},{"type":"electronic","value":"9783031721205"}],"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\/978-3-031-72120-5_50","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"533-543","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Reprogramming Distillation for\u00a0Medical Foundation Models"],"prefix":"10.1007","author":[{"given":"Yuhang","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Siyuan","family":"Du","sequence":"additional","affiliation":[]},{"given":"Haolin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jiangchao","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Ya","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yanfeng","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"50_CR1","doi-asserted-by":"crossref","unstructured":"Ahn, S., Hu, S.X., Damianou, A., Lawrence, N.D., Dai, Z.: Variational information distillation for knowledge transfer. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 9163\u20139171 (2019)","DOI":"10.1109\/CVPR.2019.00938"},{"key":"50_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data in brief 28, 104863 (2020)","journal-title":"Data in brief"},{"key":"50_CR3","doi-asserted-by":"crossref","unstructured":"Chen, D., Mei, J.P., Zhang, Y., Wang, C., Wang, Z., Feng, Y., Chen, C.: Cross-layer distillation with semantic calibration. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a035, pp. 7028\u20137036 (2021)","DOI":"10.1609\/aaai.v35i8.16865"},{"key":"50_CR4","unstructured":"Chen, P.Y.: Model reprogramming: Resource-efficient cross-domain machine learning. arXiv preprint arXiv:2202.10629 (2022)"},{"key":"50_CR5","first-page":"16664","volume":"35","author":"S Chen","year":"2022","unstructured":"Chen, S., Ge, C., Tong, Z., Wang, J., Song, Y., Wang, J., Luo, P.: Adaptformer: Adapting vision transformers for scalable visual recognition. Advances in Neural Information Processing Systems 35, 16664\u201316678 (2022)","journal-title":"Advances in Neural Information Processing Systems"},{"key":"50_CR6","unstructured":"Cohen, J.P., Morrison, P., Dao, L.: Covid-19 image data collection. arXiv preprint arXiv:2003.11597 (2020)"},{"key":"50_CR7","unstructured":"Dong, B., Zhou, P., Yan, S., Zuo, W.: Lpt: Long-tailed prompt tuning for image classification. arXiv preprint arXiv:2210.01033 (2022)"},{"key":"50_CR8","doi-asserted-by":"crossref","unstructured":"Ge, L., Hu, C., et\u00a0al.: Discrepancy and uncertainty aware denoising knowledge distillation for zero-shot cross-lingual named entity recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a038, pp. 18056\u201318064 (2024)","DOI":"10.1609\/aaai.v38i16.29762"},{"key":"50_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"50_CR10","doi-asserted-by":"crossref","unstructured":"He, X., Li, C., Zhang, P., Yang, J., Wang, X.E.: Parameter-efficient model adaptation for vision transformers. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a037, pp. 817\u2013825 (2023)","DOI":"10.1609\/aaai.v37i1.25160"},{"key":"50_CR11","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"50_CR12","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)"},{"key":"50_CR13","doi-asserted-by":"crossref","unstructured":"Jia, M., Tang, L., Chen, B.C., Cardie, C., Belongie, S., et\u00a0al.: Visual prompt tuning. In: European Conference on Computer Vision. pp. 709\u2013727. Springer (2022)","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"50_CR14","unstructured":"Kornblith, S., Norouzi, M., Lee, H., Hinton, G.: Similarity of neural network representations revisited. In: International conference on machine learning. pp. 3519\u20133529. PMLR (2019)"},{"key":"50_CR15","doi-asserted-by":"crossref","unstructured":"Li, X.L., Liang, P.: Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021)","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"50_CR16","doi-asserted-by":"crossref","unstructured":"Lin, W., Zhao, Z., Zhang, X., Wu, C., Zhang, Y., Wang, Y., Xie, W.: Pmc-clip: Contrastive language-image pre-training using biomedical documents. arXiv preprint arXiv:2303.07240 (2023)","DOI":"10.1007\/978-3-031-43993-3_51"},{"key":"50_CR17","unstructured":"Liu, X., Li, L., Li, C., Yao, A.: Norm: Knowledge distillation via n-to-one representation matching. arXiv preprint arXiv:2305.13803 (2023)"},{"key":"50_CR18","doi-asserted-by":"crossref","unstructured":"Mei, X., Liu, Z., Robson, P.M., Marinelli, B., Huang, M., Doshi, A., Jacobi, A., Cao, C., et\u00a0al.: Radimagenet: an open radiologic deep learning research dataset for effective transfer learning. Radiology: Artificial Intelligence 4(5), e210315 (2022)","DOI":"10.1148\/ryai.210315"},{"key":"50_CR19","unstructured":"Nguyen, D.M., Nguyen, H., Diep, N.T., Pham, T.N., et\u00a0al.: Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second-order graph matching. arXiv preprint arXiv:2306.11925 (2023)"},{"key":"50_CR20","unstructured":"Nguyen, T., Raghu, M., Kornblith, S.: Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth. arXiv preprint arXiv:2010.15327 (2020)"},{"key":"50_CR21","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 3967\u20133976 (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"50_CR22","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)"},{"key":"50_CR23","doi-asserted-by":"crossref","unstructured":"Saleh, A., Sukaik, R., Abu-Naser, S.S.: Brain tumor classification using deep learning. In: 2020 International Conference on Assistive and Rehabilitation Technologies (iCareTech). pp. 131\u2013136 (2020). 10.1109\/iCareTech49914.2020.00032","DOI":"10.1109\/iCareTech49914.2020.00032"},{"key":"50_CR24","doi-asserted-by":"crossref","unstructured":"Somepalli, G., Fowl, L., Bansal, A., Yeh-Chiang, P., Dar, Y., et\u00a0al.: Can neural nets learn the same model twice? investigating reproducibility and double descent from the decision boundary perspective. In: Proceedings of the ieee\/cvf conference on computer vision and pattern recognition. pp. 13699\u201313708 (2022)","DOI":"10.1109\/CVPR52688.2022.01333"},{"key":"50_CR25","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive representation distillation. arXiv preprint arXiv:1910.10699 (2019)"},{"issue":"1","key":"50_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5(1), \u00a01\u20139 (2018)","journal-title":"Scientific data"},{"key":"50_CR27","doi-asserted-by":"crossref","unstructured":"Tu, C.H., Mai, Z., Chao, W.L.: Visual query tuning: Towards effective usage of intermediate representations for parameter and memory efficient transfer learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 7725\u20137735 (2023)","DOI":"10.1109\/CVPR52729.2023.00746"},{"key":"50_CR28","doi-asserted-by":"crossref","unstructured":"Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 1365\u20131374 (2019)","DOI":"10.1109\/ICCV.2019.00145"},{"key":"50_CR29","unstructured":"Xin, Y., Luo, S., Zhou, H., Du, J., Liu, X., Fan, Y., Li, Q., Du, Y.: Parameter-efficient fine-tuning for pre-trained vision models: A survey. arXiv preprint arXiv:2402.02242 (2024)"},{"key":"50_CR30","unstructured":"Xingyi, Y., Xuehai, H., Jinyu, Z., Yichen, Z., et\u00a0al.: Covid-ct-dataset: a ct image dataset about covid-19. arXiv preprint arXiv:2003.13865 (2020)"},{"key":"50_CR31","unstructured":"Xu, S., Yao, J., Luo, R., Zhang, S., Lian, Z., Tan, M., Han, B., Wang, Y.: Towards efficient task-driven model reprogramming with foundation models. arXiv preprint arXiv:2304.02263 (2023)"},{"key":"50_CR32","doi-asserted-by":"crossref","unstructured":"Yang, Z., Li, Z., Shao, M., Shi, D., Yuan, Z., Yuan, C.: Masked generative distillation. In: European Conference on Computer Vision. pp. 53\u201369. Springer (2022)","DOI":"10.1007\/978-3-031-20083-0_4"},{"issue":"7","key":"50_CR33","first-page":"6866","volume":"35","author":"J Yao","year":"2022","unstructured":"Yao, J., Zhang, S., Yao, Y., Wang, F., Ma, J., Zhang, J., Chu, Y., Ji, L., Jia, K., et\u00a0al.: Edge-cloud polarization and collaboration: A comprehensive survey for ai. IEEE Transactions on Knowledge and Data Engineering 35(7), 6866\u20136886 (2022)","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"50_CR34","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016)"},{"key":"50_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Li, H., Du, S., Yao, J., Zhang, Y., Wang, Y.: Low-rank knowledge decomposition for medical foundation models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 11611\u201311620 (2024)","DOI":"10.1109\/CVPR52733.2024.01103"},{"key":"50_CR36","unstructured":"Zhou, Y., Zhao, Z., Du, S., Yao, J., Zhang, Y., Wang, Y., et\u00a0al.: Exploring training on heterogeneous data with mixture of low-rank adapters. In: Forty-first International Conference on Machine Learning"},{"key":"50_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Shen, Z., Zhao, Z., Wang, S., Wang, X., Zhao, X., Shen, D., Wang, Q.: Melo: Low-rank adaptation is better than fine-tuning for medical image diagnosis. arXiv preprint arXiv:2311.08236 (2023)","DOI":"10.1109\/ISBI56570.2024.10635615"}],"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\/978-3-031-72120-5_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:28:25Z","timestamp":1727872105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72120-5_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721199","9783031721205"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72120-5_50","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"}}]}}