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arXiv Computation and Language By Joey Guo, Rayan Ansari, Alaa Youssef

CheXPoT: Post-Hoc Visual Verification and Correction of VLM-Generated Radiology Reports

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CheXPoT is a post‑hoc visual verification framework that refines vision‑language model (VLM) generated chest radiograph reports by decomposing them into predicates, grounding each against DenseNet‑121 and Grad‑CAM evidence, and removing unsupported findings. On a held‑out set of 4,145 images, CheXPoT significantly improves precision and F1 scores across multiple CheXpert classes, boosting micro‑average precision from 19.2 % to 46.8 % and micro‑average F1 from 23.0 % to 49.4 %. The method also enhances performance for other VLM generators such as LLaVA‑Med and MAIRA‑2, demonstrating that inexpensive visual verification can correct hallucinations and improve report quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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