CGRL: Concept-Guided Pruning and Representation Learning for Whole-Slide Image Classification
Weakly supervised whole-slide image (WSI) classification is widely used in computational pathology because slide-level labels are easier to obtain than dense region annotations. Existing multiple instance learning (MIL) methods often aggregate large bags of patch embeddings using mainly visual cues, which can retain many non-informative patches and provide weak alignment between instance features and class-level disease semantics. We propose Concept-Guided Pruning and Representation Learning (CGRL), a simple framework that introduces class-level concept prototypes derived from disease prompts into the MIL pipeline. First, concept-relevance pruning ranks patch instances by their similarity...
| Authors | Thuc Huynh, Tuan Le, Doanh C. Bui |
|---|---|
| Primary category | cs.CV |
| Published | 14 July 2026 |
| Last updated at source | 14 July 2026 |
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