ArticleProceedings of the National Academy of Sciences of the United States of America2024
Sharing massive biomedical data at magnitudes lower bandwidth using implicit neural function.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Rapid robust high-fidelity 3D neuronal extraction from multiview calcium imaging datasets.Nature methods · 2026Article
- [A frequency-adaptive implicit neural representation method for medical image compression].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Reducing bulky medical images via shape-texture decoupled deep neural networks.Nature communications · 2026Article
- A lung CT vision foundation model facilitating disease diagnosis and medical imaging.Nature communications · 2025Article
- Semantic redundancy-aware implicit neural compression for multidimensional biomedical image data.Communications biology · 2024Article
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12 authors.
Funding
Abstract
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[Formula: see text]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.
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