EliSeg: Verified Target Construction for Report-Grounded Abnormality Segmentation

TL;DR - EliSeg is an actor–verify–revise framework for segmenting abnormalities in chest X-rays directly from raw radiology reports, without any target identity, point, or box prompt. It matters because it removes the "hidden target oracle" assumption in prior segmentation work, forcing the model to decide which findings are actually eligible targets and how many masks to produce.

• Defines report-grounded abnormality segmentation: the model must resolve target eligibility, cardinality, and finding-to-mask correspondence from an unfiltered report that may contain negated, prior, uncertain, or irrelevant findings.
• Architecture: a grammar-constrained Actor proposes target slots and masks; an independent text-only Verifier reconstructs the eligible finding inventory; Revision re-executes the shared Actor only when the two target structures disagree.
• On MIMIC-CXR-ILS it reportedly outperforms both direct segmentation methods and extract-then-segment cascades across findings, while suppressing masks for ineligible report mentions; CheXlocalize is used to show external transfer.
• Ablations are reported as confirming that verification and revision play complementary roles; code is released at github.com/Maybach-dream/EliSeg.