Multi-Source Domain Generalization for Learned Lossless Volumetric Biomedical Image Compression

Abstract

Learned lossless compression methods for volumetric biomedical images have achieved significant performance improvements compared with the traditional ones. However, they often perform poorly when applied to unseen domains due to domain gap issues. To address this problem, we propose a multi-source domain generalization method to handle two main sources of domain gap issues: modality and structure differences. To address modality differences, we develop an adaptive modality transfer (AMT) module, which predicts a set of modality-specific parameters from the original image and embeds them into the bit stream. These parameters control the weights of a mixture of experts to create a dynamic convolution, which is then used for entropy coding to facilitate modality transfer. To address structure differences, we design an adaptive structure transfer (AST) module, which decomposes the high dynamic range biomedical images into least significant bits (LSB) and most significant bits (MSB) in the wavelet domain. The MSB information, which is unique to the test image, is then used to predict an additional set of dynamic convolutions to enable structure transfer. Experimental results show that our approach reduces performance degradation caused by the domain gap to within 3% across various volumetric biomedical modalities. This paves the way for the practical end-to-end biomedical image compression.