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Related to 3D data formats, meshes, point clouds and others are used to represent the anatomical structures, each with unique applications. To better capture the spatial information and address data scarcity, self- and semi-supervised learning methods have emerged. However, efficient 3D representation learning remains challenging. Recently, Transformers have shown promise, leveraging the self-attention mechanisms that perform well on transfer learning and self-supervised methods. These techniques are applied for medical domains without extensive manual labeling. This work explores data-efficient models, scalable deep learning, semantic context utilization and transferability in 3D medical image analysis. We also evaluated the foundational models, self-supervised pre- training, transfer learning and prompt tuning, thus advancing this critical field. <\/jats:p>","DOI":"10.1142\/s2811032324500024","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T15:33:54Z","timestamp":1711640034000},"source":"Crossref","is-referenced-by-count":3,"title":["Efficient 3D Representation Learning for Medical Image Analysis"],"prefix":"10.1142","volume":"02","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6008-9700","authenticated-orcid":false,"given":"Yucheng","family":"Tang","sequence":"first","affiliation":[{"name":"NVIDIA Corporation, Santa Clara, CA 95051, USA"},{"name":"Vanderbilt University, Nashville, TN 37235, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, P. R. 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