EVADE: Evidence-Verified Agentic Diagnosis with Escape
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TL;DR - EVADE is a training-free diagnostic framework that makes a frozen medical vision-language model compare answers from full and self-localized zoomed image views, abstaining when they disagree. It improves calibration and selective safety without sacrificing accuracy.
- On VQA-RAD, SLAKE, and PathVQA with Qwen2.5-VL-7B, EVADE reduced expected calibration error by up to 45% versus zero-shot.
- Cross-view consistency avoids relying on textual self-verification, which can itself hallucinate.
- EVADE was the only evaluated method to improve both calibration and selective risk while maintaining accuracy.
- Self-proposed crops localized diagnostic structures better than center or random crops, but reliability gains came from agreement gating and abstention rather than answer revision.
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EVADE: Evidence-Verified Agentic Diagnosis with Escape
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Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - EVADE is a training-free diagnostic framework that makes a frozen medical vision-language model compare answers from full and self-localized zoomed image views, abstaining when they disagree. It improves calibration and selective safety without sacrificing accuracy.
- On VQA-RAD, SLAKE, and PathVQA with Qwen2.5-VL-7B, EVADE reduced expected calibration error by up to 45% versus zero-shot.
- Cross-view consistency avoids relying on textual self-verification, which can itself hallucinate.
- EVADE was the only evaluated method to improve both calibration and selective risk while maintaining accuracy.
- Self-proposed crops localized diagnostic structures better than center or random crops, but reliability gains came from agreement gating and abstention rather than answer revision.