A Content-Aware Variable-Rate Framework for Pathology Learned Image Compression (Patholic).

The substantial size of gigapixel whole slide images (WSIs) presents significant challenges in terms of data storage, transfer, and computational analysis. Existing image compression methods yield suboptimal compression ratios because they (1) overlook redundancy across neighboring/similar patches, and (2) apply uniform compression without considering content differences. To address these issues, we introduce PathoLIC (Pathology Learned Image Compression), a novel learning-based variable-rate compression framework tailored for WSI. Specifically, PathoLIC initially assigns a content score to each non-overlapping patch in the WSI, which reflects its diagnostic relevance. The compression level for each patch is determined based on the content scores, prioritizing detail preservation in diagnostically important regions, e.g., tumor area, while compressing more on less informative regions, e.g., stroma and background. Furthermore, PathoLIC employs attention mechanisms to capture relationships between neighboring or similar patches, which minimize redundancy by compressing shared features. Experimental results demonstrate that PathoLIC achieves over 8 ×  compression beyond the standard Aperio SVS format while preserving image details. Moreover, it maintains strong performance across various downstream tasks, such as patch-level (WSI-level) cancer subtyping and nuclei segmentation. These results demonstrate its potential for large-scale WSI data management. The source code will be released at https://github.com/wqli498/PathoLIC.