Sharing massive biomedical data at magnitudes lower bandwidth using implicit neural function

• 成果类型: Article
• 署名作者: Yang, Runzhao; Xiao, Tingxiong; Cheng, Yuxiao; Li, Anan; Qu, Jinyuan; Liang, Rui; Bao, Shengda; Wang, Xiaofeng; Wang, Jue; Suo, Jinli; Luo, Qingming; Dai, Qionghai
• 署名单位: Tsinghua University; Tsinghua University; Huazhong University of Science & Technology; Huazhong University of Science & Technology; Huazhong University of Science & Technology; Hainan University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13929
• DOI: 10.1073/pnas.2320870121
• 发表日期: 2024-07-09
• 关键词: compression; reconstruction; REPRESENTATION; Visualization; tomography; annotation; atlas; scale

摘要

Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging deep learning-based methods demand huge training data and are difficult to generalize. Here, we propose to conduct Biomedical data compRession with Implicit nEural Function (BRIEF) by representing the target data with compact neural networks, which are data specific and thus have no generalization issues. Benefiting from the strong representation capability of implicit neural function, BRIEF achieves 2 similar to 3 orders of magnitude compression on diverse biomedical data at significantly higher fidelity than existing techniques. Besides, BRIEF is of consistent performance across the whole data volume, and supports customized spatially varying fidelity. BRIEF's multifold advantageous features also serve reliable downstream tasks at low bandwidth. Our approach will facilitate low-bandwidth data sharing and promote collaboration and progress in the biomedical field.