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Instance-Guided Report Anchoring for Text-Free 3D Abnormality Segmentation in Chest CT

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Accurate 3D abnormality segmentation in chest CT requires dense spatial supervision, but obtaining expert voxel-level labels is costly. Radiology reports, however, are routinely generated during clinical interpretation and contain instance-specific descriptions that can provide additional guidance without new dense annotation. Existing vision-language grounding methods typically require report-derived findings at inference, making localization dependent on paired text and limiting each forward pass to a queried finding. We propose Instance-Guided Report Anchoring (IGRA), a model-agnostic modul

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