Papers by Lingran Song

2 papers
Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback (2025.acl-long)

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Challenge: Existing Medical Large Vision-Language Models (Med-LVLMs) lack visual localization in medical images, which is crucial for abnormality detection and interpretation.
Approach: They propose a medical abnormalities unveiling method based on a Medical Abnormalities Unveiler dataset and propose 'abnormal-aware instruction tuning' and 'abbnormal-Aware Reward' method generates diagnoses based upon identified abnormal areas in medical images.
Outcome: The proposed method outperforms existing medical large vision-language models in identifying and understanding medical abnormalities and improves generalization capability.
MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration (2025.findings-acl)

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Challenge: Recent advances in medical Large Language Models have demonstrated powerful reasoning and diagnostic capabilities.
Approach: They propose a modular multi-agent framework for multi-modal medical diagnosis . they decompose the medical diagnostic process into specialized roles .
Outcome: The framework decomposes the medical diagnostic process into specialized roles . it achieves significant performance improvements ranging from 18% to 365% compared to baseline models.

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