VideoPath-LLaVA: Pathology Diagnostic Reasoning Through Video Instruction Tuning

2025-05-07

AI Summary

This work addresses the lack of video-modal modeling capability in computational pathology by introducing the first video-multimodal large language model for pathological diagnosis. Methodologically, it integrates single-slide images, automatically extracted key-frame video clips, and manually segmented pathological videos to emulate the clinician’s natural workflow—“viewing slides → describing findings → reasoning → diagnosing.” It pioneers temporal video modeling in pathology analysis, constructs the first video-chain-of-thought instruction dataset (VideoPath-Instruct), and proposes a two-stage transfer learning paradigm to mitigate annotation scarcity. Built upon LLaVA, the model extends the visual encoder with CLIP-ViP features, temporal attention mechanisms, and instruction tuning to enable multi-granularity perception and generative diagnostic reasoning. The approach establishes a new benchmark on pathological video diagnosis, achieving significant improvements in both accuracy and interpretability. All code, data, and models are publicly released.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We present VideoPath-LLaVA, the first large multimodal model (LMM) in computational pathology that integrates three distinct image scenarios, single patch images, automatically keyframe-extracted clips, and manually segmented video pathology images, to mimic the natural diagnostic process of pathologists. By generating detailed histological descriptions and culminating in a definitive sign-out diagnosis, VideoPath-LLaVA bridges visual narratives with diagnostic reasoning. Central to our approach is the VideoPath-Instruct dataset, comprising 4278 video and diagnosis-specific chain-of-thought instructional pairs sourced from educational histopathology videos on YouTube. Although high-quality data is critical for enhancing diagnostic reasoning, its creation is time-intensive and limited in volume. To overcome this challenge, we transfer knowledge from existing single-image instruction datasets to train on weakly annotated, keyframe-extracted clips, followed by fine-tuning on manually segmented videos. VideoPath-LLaVA establishes a new benchmark in pathology video analysis and offers a promising foundation for future AI systems that support clinical decision-making through integrated visual and diagnostic reasoning. Our code, data, and model are publicly available at https://github.com/trinhvg/VideoPath-LLaVA.

Problem

• Develops first multimodal model for pathology diagnostic reasoning
• Integrates diverse image scenarios to mimic pathologists' process
• Addresses limited high-quality data via knowledge transfer

Innovation

• Integrates single patch, keyframe clips, segmented videos
• Uses VideoPath-Instruct dataset with 4278 instructional pairs
• Transfers knowledge from single-image to video datasets