A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model

TL;DR

This paper introduces SmartPath-R1, a versatile multimodal large language model for pathology that can perform multiple diagnostic tasks with enhanced reasoning, eliminating the need for expensive annotations and addressing a broad spectrum of clinical pathology applications.

Contribution

The study presents a novel reasoning-enhanced multimodal LLM that handles diverse pathology tasks without chain-of-thought supervision, using scale-dependent fine-tuning and a mixture-of-experts mechanism.

Findings

Outperforms existing models on 72 pathology tasks

Handles both ROI-level and WSI-level analyses effectively

Achieves robust reasoning without chain-of-thought annotations

Abstract

Multimodal large language models (MLLMs) have emerged as powerful tools for computational pathology, offering unprecedented opportunities to integrate pathological images with language context for comprehensive diagnostic analysis. These models hold particular promise for automating complex tasks that traditionally require expert interpretation of pathologists. However, current MLLM approaches in pathology demonstrate significantly constrained reasoning capabilities, primarily due to their reliance on expensive chain-of-thought annotations. Additionally, existing methods remain limited to simplex application of visual question answering (VQA) at the region-of-interest (ROI) level, failing to address the full spectrum of diagnostic needs such as ROI classification, detection, segmentation, whole-slide-image (WSI) classification and VQA in clinical practice. In this study, we present SmartPath-R1, a versatile MLLM capable of simultaneously addressing both ROI-level and WSI-level tasks while demonstrating robust pathological reasoning capability. Our framework combines scale-dependent supervised fine-tuning and task-aware reinforcement fine-tuning, which circumvents the requirement for chain-of-thought supervision by leveraging the intrinsic knowledge within MLLM. Furthermore, SmartPath-R1 integrates multiscale and multitask analysis through a mixture-of-experts mechanism, enabling dynamic processing for diverse tasks. We curate a large-scale dataset comprising 2.3M ROI samples and 188K WSI samples for training and evaluation. Extensive experiments across 72 tasks validate the effectiveness and superiority of the proposed approach. This work represents a significant step toward developing versatile, reasoning-enhanced AI systems for precision pathology.