Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction

arXiv:2402.19326v1 Announce Type: new
Abstract: Whole Slide Image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently, Vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However, existing methods leverage coarse-grained pathogenetic descriptions for visual representation supervision, which are insufficient to capture the complex visual appearance of pathogenetic images, hindering the generalizability of models on diverse downstream tasks. Additionally, processing high-resolution WSIs can be computationally expensive. In this paper, we propose a novel "Fine-grained Visual-Semantic …

abstract arxiv classification cs.cv fine-grained image instance language language models mil multiple performance representation semantic supervision type vision vision-language models visual vlms