{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:00:25Z","timestamp":1777042825443,"version":"3.51.4"},"reference-count":70,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council of Canada (NSERC)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Medical imaging tasks are very challenging due to the lack of publicly available labeled datasets. Hence, it is difficult to achieve high performance with existing deep learning models as they require a massive labeled dataset to be trained effectively. An alternative solution is to use pre-trained models and fine-tune them using a medical imaging dataset. However, all existing models are pre-trained using natural images, which represent a different domain from that of medical imaging; this leads to poor performance due to domain shift. To overcome these problems, we propose a pre-trained backbone using a collected medical imaging dataset with a self-supervised learning tool called a masked autoencoder. This backbone can be used as a pre-trained model for any medical imaging task, as it is trained to learn a visual representation of different types of medical images. To evaluate the performance of the proposed backbone, we use four different medical imaging tasks. The results are compared with existing pre-trained models. These experiments show the superiority of our proposed backbone in medical imaging tasks.<\/jats:p>","DOI":"10.3390\/computation13040088","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T16:26:31Z","timestamp":1743611191000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks"],"prefix":"10.3390","volume":"13","author":[{"given":"Anubhav","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9935-2417","authenticated-orcid":false,"given":"Islam","family":"Osman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8464-8650","authenticated-orcid":false,"given":"Mohamed S.","family":"Shehata","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9299-9602","authenticated-orcid":false,"given":"W. John","family":"Braun","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-9807","authenticated-orcid":false,"given":"Rebecca E.","family":"Feldman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"ref_1","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., and Girshick, R. (2022, January 18\u201324). 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