arXiv 论文提出反事实病灶擦除法，MedScope-4B 医学视觉语言模型大幅减少幻觉报告
Seeing Is Not Believing: Counterfactual Lesion Erasure Teaches a Small Medical Vision-Language Agent to Report Only What the Image Shows
AI 导读
arXiv 论文（arXiv:2611.13006）提出用 inpainting 擦除病灶构建反事实图像，并以证据敏感度差距（ESG）衡量医学 VLM 是否只报告图像真实所见。
正文 · 原文
Abstract:Medical vision-language models (VLMs) often report a finding because the question names it; standard benchmarks miss this. From public segmentation datasets we build counterfactual views without manual annotation: the lesion erased by inpainting, a same-shape control edit elsewhere, and unedited lesion-free images. The evidence sensitivity gap (ESG) is the rate of absent-or-refer answers after erasure minus the rate after the control edit; it is zero for a model that ignores the image or reacts to both edits alike. MedScope-4B (Qwen3.5-4B, LoRA, image tools) was trained on label-derived trajectories, selected by a pre-registered rule and tested once against nine frontier and six open VLMs; four pre-registered checks then probed the mechanism. On 507 internal cases across five imaging types, MedScope-4B reached ESG 0.758 and claimed an erased lesion in 9.6% of cases, against 0.27-0.47 and 41-62% for the frontier models (all p<0.001), without significant loss of accuracy on real lesions. Without counterfactual views ESG fell to 0.279+-0.107 (three seeds); the effect replicated on MedGemma 1.5 4B and two unseen inpainting tools. At four external centres the advantage held but frontier models detected more real lesions; on a fresh external test MedScope-4B detected 0 of 48 pneumothorax films, with net utility below gemini-4-argon's (-0.36 [-0.50, -0.23]). Matching the control edit was not shown to matter; over three seeds no fresh-external ESG advantage over removal-based DPO was established; a paired-view consistency objective did not beat a matched-compute control, while that continuation alone cut erased-lesion claims on unseen tools at every seed, at a cost in sensitivity at one. Training on erased and edited images gives a small model an evidence-gated disposition that prompting does not give frontier models; it does not improve perception, and rare critical findings need coverage.
| Comments: | 28 pages, 1 figure, 13 tables. Preprint v1.0; reader study in progress. Code, data recipes, pre-registrations and per-view outputs: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) |
| ACM classes: | I.2.10; I.4.8; J.3 |
| Cite as: | arXiv:2611.13006 [cs.AI] |
| (or arXiv:2611.13006v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13006 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bhaskar Gurram [view email]
[v1]
Sun, 8 Nov 2026 23:09:18 UTC (1,706 KB)
来源：arXiv cs.AI · arxiv.org