用原型引导精炼病理切片粗标注，提升细粒度分割精度。
A Prototype-Guided Coarse Annotations Refining Approach for Whole Slide Images
- 构建局部到全局的代表性原型，捕捉切片内外语义关系。
- 在三个癌症数据集上显著优于现有最先进方法。
- 适合医学图像分割、弱监督学习研究者参考。
全切片图像（WSI）中的细粒度标注能精确标识不同病理区域边界，但生成成本高；而粗标注相对容易。现有精炼方法依赖大量训练样本或干净数据集，难以捕捉切片内与切片间的潜在语义模式，限制了精度。本文提出一种原型引导的精炼方法：首先通过联合建模切片内局部语义与切片间上下文关系，构建非冗余代表原型；随后设计原型引导的伪标签模块以精炼粗标注；最后采用动态数据采样与重微调策略训练分类器。在三个公开的WSI数据集（涵盖淋巴、肝脏和结直肠癌）上的实验表明，该方法显著优于现有SOTA方法。代码将开源。
原文摘要 · Abstract (English)
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.
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