Weakly Supervised Vector Quantization for Whole Slide Image Classification

Whole Slide Images (WSIs) are gigapixel, high-resolution digital scans of microscope slides, providing detailed tissue profiles for pathological analysis. Due to their gigapixel size and lack of detailed annotations, Multiple Instance Learning (MIL) becomes the primary technique for WSI analysis. However, current MIL methods for WSIs directly use embeddings extracted by a pretrained vision encoder, which are not task-specific and often exhibit high variability. To address this, we introduce a novel method, VQ-MIL, which maps the embeddings to a discrete space using weakly supervised vector quantization to refine the embeddings and reduce the variability. Additionally, the discrete embeddings from our methods provides clearer visualizations compared to other methods. Our experiments show that VQ-MIL achieves state-of-the-art classification results on two benchmark datasets. The source code is available at https://github.com/aCoalBall/VQMIL .