Papers with Medical Large Vision-Language Models
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning (2025.acl-long)
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| Challenge: | Existing methods rely on inference-time interventions, which are limited in attention adaptation or require additional supervision. |
| Approach: | They propose a framework for automatic attention alignment tuning that leverages weak labels from SAM and selectively modifies visually-critical attention heads to improve alignment while minimizing interference. |
| Outcome: | The proposed framework outperforms state-of-the-art models on medical VQA and report generation benchmarks. |
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. |