Saliency Guided Longitudinal Medical Visual Question Answering
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918235358625792 |
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| author | Wu, Jialin Liu, Xiaofeng |
| author_facet | Wu, Jialin Liu, Xiaofeng |
| contents | Longitudinal medical visual question answering (Diff-VQA) requires comparing paired studies from different time points and answering questions about clinically meaningful changes. In this setting, the difference signal and the consistency of visual focus across time are more informative than absolute single-image findings. We propose a saliency-guided encoder-decoder for chest X-ray Diff-VQA that turns post-hoc saliency into actionable supervision. The model first performs a lightweight near-identity affine pre-alignment to reduce nuisance motion between visits. It then executes a within-epoch two-step loop: step 1 extracts a medically relevant keyword from the answer and generates keyword-conditioned Grad-CAM on both images to obtain disease-focused saliency; step 2 applies the shared saliency mask to both time points and generates the final answer. This closes the language-vision loop so that the terms that matter also guide where the model looks, enforcing spatially consistent attention on corresponding anatomy. On Medical-Diff-VQA, the approach attains competitive performance on BLEU, ROUGE-L, CIDEr, and METEOR while providing intrinsic interpretability. Notably, the backbone and decoder are general-domain pretrained without radiology-specific pretraining, highlighting practicality and transferability. These results support saliency-conditioned generation with mild pre-alignment as a principled framework for longitudinal reasoning in medical VQA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25374 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Saliency Guided Longitudinal Medical Visual Question Answering Wu, Jialin Liu, Xiaofeng Artificial Intelligence Computer Vision and Pattern Recognition Longitudinal medical visual question answering (Diff-VQA) requires comparing paired studies from different time points and answering questions about clinically meaningful changes. In this setting, the difference signal and the consistency of visual focus across time are more informative than absolute single-image findings. We propose a saliency-guided encoder-decoder for chest X-ray Diff-VQA that turns post-hoc saliency into actionable supervision. The model first performs a lightweight near-identity affine pre-alignment to reduce nuisance motion between visits. It then executes a within-epoch two-step loop: step 1 extracts a medically relevant keyword from the answer and generates keyword-conditioned Grad-CAM on both images to obtain disease-focused saliency; step 2 applies the shared saliency mask to both time points and generates the final answer. This closes the language-vision loop so that the terms that matter also guide where the model looks, enforcing spatially consistent attention on corresponding anatomy. On Medical-Diff-VQA, the approach attains competitive performance on BLEU, ROUGE-L, CIDEr, and METEOR while providing intrinsic interpretability. Notably, the backbone and decoder are general-domain pretrained without radiology-specific pretraining, highlighting practicality and transferability. These results support saliency-conditioned generation with mild pre-alignment as a principled framework for longitudinal reasoning in medical VQA. |
| title | Saliency Guided Longitudinal Medical Visual Question Answering |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.25374 |