{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T21:58:23Z","timestamp":1777759103508,"version":"3.51.4"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031966248","type":"print"},{"value":"9783031966255","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"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\/978-3-031-96625-5_12","type":"book-chapter","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T07:06:58Z","timestamp":1754464018000},"page":"172-185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diffusion MAE: Paving the\u00a0Way for\u00a0Representation Learning of\u00a0Diffusion MRI"],"prefix":"10.1007","author":[{"given":"Haotian","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Chatterjee, S., et al.: ShuffleUNet: super resolution of diffusion-weighted MRIs using deep learning. In: 2021 29th European Signal Processing Conference, pp. 940\u2013944. IEEE (2021)","DOI":"10.23919\/EUSIPCO54536.2021.9615963"},{"key":"12_CR2","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.media.2019.06.010","volume":"57","author":"G Chen","year":"2019","unstructured":"Chen, G., Dong, B., Zhang, Y., Lin, W., Shen, D., Yap, P.T.: XQ-SR: joint xq space super-resolution with application to infant diffusion MRI. Med. Image Anal. 57, 44\u201355 (2019)","journal-title":"Med. Image Anal."},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Chen, G., et\u00a0al.: Estimating tissue microstructure with undersampled diffusion data via graph convolutional neural networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 280\u2013290. Springer (2020)","DOI":"10.1007\/978-3-030-59728-3_28"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Chen, G., et al.: Hybrid graph transformer for tissue microstructure estimation with undersampled diffusion MRI data. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 113\u2013122. Springer (2022)","DOI":"10.1007\/978-3-031-16431-6_11"},{"key":"12_CR5","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"12_CR6","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.neuroimage.2014.10.026","volume":"105","author":"A Daducci","year":"2015","unstructured":"Daducci, A., Canales-Rodr\u00edguez, E.J., Zhang, H., Dyrby, T.B., Alexander, D.C., Thiran, J.P.: Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data. Neuroimage 105, 32\u201344 (2015)","journal-title":"Neuroimage"},{"key":"12_CR7","unstructured":"Dosovitskiy, A.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 574\u2013584, January 2022","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"12_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":"12_CR10","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"12_CR11","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1016\/j.neuroimage.2018.04.017","volume":"185","author":"M Ouyang","year":"2019","unstructured":"Ouyang, M., Dubois, J., Yu, Q., Mukherjee, P., Huang, H.: Delineation of early brain development from fetuses to infants with diffusion MRI and beyond. Neuroimage 185, 836\u2013850 (2019)","journal-title":"Neuroimage"},{"key":"12_CR12","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.mri.2024.03.030","volume":"109","author":"K Sakaie","year":"2024","unstructured":"Sakaie, K., et al.: Multi-shell diffusion MRI of the fornix as a biomarker for cognition in Alzheimer\u2019s disease. Magn. Reson. Imaging 109, 221\u2013226 (2024)","journal-title":"Magn. Reson. Imaging"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Shaker, A.M., Maaz, M., Rasheed, H., Khan, S., Yang, M.H., Khan, F.S.: UNETR++: delving into efficient and accurate 3D medical image segmentation. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3398728"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Tang, F., et al.: Hyspark: hybrid sparse masking for large scale medical image pre-training. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 330\u2013340. Springer (2024)","DOI":"10.1007\/978-3-031-72120-5_31"},{"key":"12_CR15","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems vol. 30 (2017)"},{"key":"12_CR16","doi-asserted-by":"publisher","first-page":"116137","DOI":"10.1016\/j.neuroimage.2019.116137","volume":"202","author":"JD Tournier","year":"2019","unstructured":"Tournier, J.D., et al.: Mrtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202, 116137 (2019)","journal-title":"Neuroimage"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Van\u00a0Essen, D.C., et\u00a0al.: The WU-Minn human connectome project: an overview. NeuroImage 80, 62\u201379 (2013)","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"key":"12_CR18","doi-asserted-by":"publisher","first-page":"101559","DOI":"10.1016\/j.media.2019.101559","volume":"58","author":"J Wasserthal","year":"2019","unstructured":"Wasserthal, J., Neher, P.F., Hirjak, D., Maier-Hein, K.H.: Combined tract segmentation and orientation mapping for bundle-specific tractography. Med. Image Anal. 58, 101559 (2019)","journal-title":"Med. Image Anal."},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Wei, Y., Gupta, A., Morgado, P.: Towards latent masked image modeling for self-supervised visual representation learning. In: European Conference on Computer Vision, pp. 1\u201317. Springer (2025)","DOI":"10.1007\/978-3-031-72933-1_1"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: A registration-and uncertainty-based framework for white matter tract segmentation with only one annotated subject. In: 2023 IEEE 20th International Symposium on Biomedical Imaging, pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230415"},{"key":"12_CR21","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.media.2017.09.001","volume":"42","author":"C Ye","year":"2017","unstructured":"Ye, C.: Tissue microstructure estimation using a deep network inspired by a dictionary-based framework. Med. Image Anal. 42, 288\u2013299 (2017)","journal-title":"Med. Image Anal."},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Zhou, H.Y., et al.: nnFormer: volumetric medical image segmentation via a 3D Transformer. IEEE Trans. Image Process. (2023)","DOI":"10.1109\/TIP.2023.3293771"},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, L., Liu, H., Bae, J., He, J., Samaras, D., Prasanna, P.: Self pre-training with masked autoencoders for medical image classification and segmentation. In: 2023 IEEE 20th International Symposium on Biomedical Imaging, pp.\u00a01\u20136. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230477"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96625-5_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T13:21:44Z","timestamp":1777468904000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96625-5_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9783031966248","9783031966255"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96625-5_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]},"assertion":[{"value":"7 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"25 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmi2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}