SLPD: Slide-level Prototypical Distillation for WSIs
Zhimiao Yu Tiancheng Lin Yi Xu
Improving the feature representation ability is the foundation of many whole
slide pathological image (WSIs) tasks. Recent works have achieved great success
in pathological-specific self-supervised learning (SSL). However, most of them
only focus on learning patch-level representations, thus there is still a gap
between pretext and slide-level downstream tasks, e.g., subtyping, grading and
staging. Aiming towards slide-level representations, we propose Slide-Level
Prototypical Distillation (SLPD) to explore intra- and inter-slide semantic
structures for context modeling on WSIs. Specifically, we iteratively perform
intra-slide clustering for the regions (4096x4096 patches) within each WSI to
yield the prototypes and encourage the region representations to be closer to
the assigned prototypes. By representing each slide with its prototypes, we
further select similar slides by the set distance of prototypes and assign the
regions by cross-slide prototypes for distillation. SLPD achieves
state-of-the-art results on multiple slide-level benchmarks and demonstrates
that representation learning of semantic structures of slides can make a
suitable proxy task for WSI analysis. Code will be available at
https://github.com/Carboxy/SLPD.