ΔRepresentation models phenotypes as representation increments over normal anatomy via counterfactual reasoning, improving lesion grounding and phenotype recognition across six medical VLMs
The work proposes ΔRepresentation, a visual phenotype representation learning framework for medical VLMs: BaseAnatomy applies geometry supervision to normal anatomical representations using ideal spatial distributions, while Phenotype uses counterfactual reasoning to estimate a normal anatomical reference for each pathological patch and learn phenotype-specific representation increments that cluster by phenotype. On ReXGroundingCT and LIDC-IDRI, it consistently improves lesion grounding and phenotype recognition across six pretrained medical VLMs.