Sharing massive biomedical data at magnitudes lower bandwidth using implicit neural function.
Proc Natl Acad Sci U S A
; 121(28): e2320870121, 2024 Jul 09.
Article
in En
| MEDLINE
| ID: mdl-38959033
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Neural Networks, Computer
/
Information Dissemination
Limits:
Humans
Language:
En
Journal:
Proc Natl Acad Sci U S A
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: