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Instance-Guided Report Anchoring (IGRA) is a model-agnostic module that links each abnormality instance in a chest CT to the corresponding finding in a radiology report during training, while discarding text components at inference. By reformulating free-text grounding as multi-label volumetric segmentation, IGRA allows all abnormality categories to be predicted in a single image-only forward pass. The method improves Dice scores by 22.5% over the strongest image-only baseline and matches state‑of‑the‑art performance on single-finding subsets, with consistent gains across multiple 3D segmentation backbones and datasets.
arXiv:2606. 17710v1 Announce Type: cross Abstract: Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image.
arXiv:2608.30021v1 Announce Type: cross Abstract: Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are oft...
arXiv:2604.09537v2 Announce Type: replace Abstract: A grounding verifier checks a statement about a patient's image against the radiology report and the cited evidence. The state-of-the-art methods f...
arXiv:2608. 07299v1 Announce Type: cross Abstract: Radiology reports describe clinical observations but do not specify executable segmentation targets.
arXiv:2605.30984v2 Announce Type: replace-cross Abstract: Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and...