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A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
Created by
Haebom
Author
Zhe Xu, Ziyi Liu, Junlin Hou, Jiabo Ma, Cheng Jin, Yihui Wang, Zhixuan Chen, Zhengyu Zhang, Fuxiang Huang, Zhengrui Guo, Fengtao Zhou, Yingxue Xu, Xi Wang, Ronald Cheong Kin Chan, Li Liang, Hao Chen
Outline
This paper discusses multimodal large-scale language models (MLLMs), which have emerged as powerful tools for providing comprehensive diagnostic analysis by integrating pathological images and linguistic context. Existing MLLM approaches rely on costly thought process annotations, resulting in limited inference capabilities. In this study, we present SmartPath-R1, a multi-objective MLLM that simultaneously handles ROI- and WSI-level tasks and exhibits robust pathological inference capabilities. SmartPath-R1 eliminates the need for thought process supervision by leveraging inherent knowledge within the MLLM through a combination of scale-dependent supervised learning fine-tuning and task-aware reinforcement learning fine-tuning. Furthermore, it integrates multi-scale and multi-task analysis through an expert mixture mechanism, enabling dynamic processing across diverse tasks. We validate the effectiveness and superiority of the proposed method through extensive experiments on 72 tasks using a large dataset consisting of 2.3 million ROI samples and 188,000 WSI samples.
Takeaways, Limitations
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Takeaways:
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We present SmartPath-R1, a multi-purpose MLLM that can simultaneously handle various tasks (ROI classification, detection, segmentation, WSI classification, VQA, etc.) in the field of pathology.
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Improve reasoning ability by leveraging MLLM's unique knowledge without annotating the thought process.
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Efficient processing and performance enhancement through multi-scale and multi-task analysis.
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Performance validation through extensive experiments using large-scale datasets.
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Significant progress toward developing robust, reasoning-enhanced AI systems for precision pathology.
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Limitations:
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The performance of SmartPath-R1 can be highly dependent on the quality and quantity of the large dataset used. Dataset bias can impact model performance.
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Further research is needed to evaluate the generalizability of the proposed method. Performance evaluations are needed across a variety of pathological images and clinical settings.
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Additional validation and safety evaluation are needed for application in actual clinical settings.
