A Prototype-Guided Coarse Annotations Refining Approach for Whole Slide Images
Journal:
arXiv
Published Date:
Mar 25, 2025
Abstract
The fine-grained annotations in whole slide images (WSIs) show the boundaries
of various pathological regions. However, generating such detailed annotation
is often costly, whereas the coarse annotations are relatively simpler to
produce. Existing methods for refining coarse annotations often rely on
extensive training samples or clean datasets, and fail to capture both
intra-slide and inter-slide latent sematic patterns, limiting their precision.
In this paper, we propose a prototype-guided approach. Specifically, we
introduce a local-to-global approach to construct non-redundant representative
prototypes by jointly modeling intra-slide local semantics and inter-slide
contextual relationships. Then a prototype-guided pseudo-labeling module is
proposed for refining coarse annotations. Finally, we employ dynamic data
sampling and re-finetuning strategy to train a patch classifier. Extensive
experiments on three publicly available WSI datasets, covering lymph, liver,
and colorectal cancers, demonstrate that our method significantly outperforms
existing state-of-the-art (SOTA) methods. The code will be available.
