A Prototype-Guided Coarse Annotations Refining Approach for Whole Slide Images
This work addresses the costly and imprecise annotation process in medical imaging, offering a domain-specific improvement for pathology analysis.
The paper tackles the problem of refining coarse annotations in whole slide images (WSIs) for pathological analysis by proposing a prototype-guided approach, which significantly outperforms existing state-of-the-art methods across three cancer datasets.
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.