Vision-Language Pretraining for Pathology Report and Whole Slide Image Joint Analysis: Development and Validation

This study presents a novel approach for joint analysis of Whole Slide Images (WSIs) and pathology reports using vision-language pretraining (VLP) models. Digital pathology faces unique challenges due to the gigapixel-level nature of WSIs and the complex, specialized language in reports. We propose a framework that leverages the power of transformers with a multi-instance learning strategy to effectively process WSIs at multiple resolutions while aligning them with their corresponding reports. Our model was trained on 10,000 WSI-report pairs from The Cancer Genome Atlas (TCGA) and validated on external datasets. Experimental results demonstrate significant performance improvements across multiple tasks, including zero-shot classification (F1 scores of 0.73-0.85), cross-modal retrieval (Recall@1 of 0.78), and pathology visual question answering (accuracy of 0.82). The model outperforms existing approaches by addressing the challenges of high-resolution images and specialized terminology through effective multimodal alignment, providing a promising foundation for computational pathology applications.