WSISum: WSI summarization via dual-level semantic reconstruction

Each gigapixel whole slide image (WSI) contains tens of thousands of patches, many of which are redundant, leading to significant computational, storage, and transmission overhead. This motivates the need for automatic WSI summarization, which aims to extract a compact subset of patches that can effectively approximate the original WSI. In this paper, we propose WSISum, a unified framework that performs WSI Summarization through dual-level semantic reconstruction. Specifically, WSISum integrates two complementary reconstruction strategies: low-level patch semantic reconstruction via clustering-based sparse sampling; and high-level slide semantic reconstruction through knowledge distillation from multiple WSI-level foundation models. Experimental results show that WSISum achieves satisfactory performance in a variety of downstream tasks, including cancer subtyping, biomarker prediction, and metastasis subtyping, while significantly reducing computational cost. Code and models are available at https://github.com/Badgewho/WSISum.