PolyPath: Adapting a Large Multimodal Model for Multislide Pathology Report Generation

The interpretation of histopathology cases underlies many important diagnostic and treatment decisions in medicine. Notably, this process typically requires pathologists to integrate and summarize findings across multiple slides per case. Existing vision-language capabilities in computational pathology have so far been largely limited to small regions of interest, larger regions at low magnification, or single whole-slide images (WSIs). This limits interpretation of findings that span multiple high-magnification regions across multiple WSIs. By making use of Gemini 1.5 Flash, a large multimodal model with a 1-million token context window, we demonstrate the ability to generate bottom-line diagnoses from up to 40,000 image patches of size 768 × 768 pixels from multiple WSIs at 10× magnification. This is the equivalent of up to 11 hours of video at 1 fps. Expert pathologist evaluations demonstrate that the generated report text is clinically accurate and equivalent to or preferred over the original reporting for 68% (95% CI, 60%-76%) of multi-slide examples with up to 5 slides. Although performance decreased for examples with ≥6 slides, this study demonstrates the promise of leveraging the long-context capabilities of modern large multimodal models for the uniquely challenging task of medical report generation where each case can contain thousands of image patches.