Sharing Massive Biomedical Data at Magnitudes Lower Bandwidth Using Implicit Neural Function
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 original 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 [~] 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, supports customized spatially-varying fidelity. BRIEFs multi-fold 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.