Attention Without Grounding: Causal Evaluation of Visual Explanations in Medical VLMs
Attention and saliency heatmaps are widely used to explain medical Vision-Language Model (VLM) outputs on chest X-rays, yet whether they truly highlight the image evidence driving predictions has not been causally tested. We audit faithfulness via overlap with radiologist bounding boxes on PadChest (n=637), attribution mass within radiologist masks on CheXlocalize (n=643), and 16x16 patch-occlusion maps that record which regions, when hidden, change the answer. We study three MedGemma-4B variants, cross-family probes on LLaVA-RAD and Qwen3-VL-8B-Instruct, and the specialist CheXagent-2-3b, with two CXR-trained classifiers (DenseNet121, ResNet50) as positive controls. A heatmap is faithful...
| Authors | Binesh Sadanandan, Vahid Behzadan |
|---|---|
| Primary category | cs.CV |
| Published | 20 July 2026 |
| Last updated at source | 11 August 2026 |
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