$Δ$Representation: Geometry Supervised Representation Learning of Phenotypes via Counterfactual Reasoning for Medical VLMs
To address this gap, we propose $Δ$Representation, a visual phenotype representation learning framework based on counterfactual reasoning for medical VLMs. It comprises BaseAnatomy, a geometry-supervised representation learning module, and $Δ$Phenotype, a counterfactual incremental representation learning module.
ProofPaper ↗
Key points
- Medical vision-language models (VLMs) have shown increasing potential for radiological image interpretation.
- Medical VLMs encode radiological images into visual representations that capture both anatomical and phenotypic information for diagnosis.
- Existing approaches improve pathological phenotype representations through semantic-guided representation alignment.
- BaseAnatomy provides fine-grained geometric supervision through spatial relationships across and within anatomical structures. $Δ$Phenotype computes the representation increment between lesion representations and their corresponding normal anatomical representations, and supervises increments associated with the same phenotype to cluster in the representation space.
Sources (1)
- [1]$Δ$Representation: Geometry Supervised Representation Learning of Phenotypes via Counterfactual Reasoning for Medical VLMsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 03:49 PM
To address this gap, we propose $Δ$Representation, a visual phenotype representation learning framework based on counterfactual reasoning for medical VLMs. It comprises BaseAnatomy, a geometry-supervised representation learning module, and $Δ$Phenotype, a counterfactual incremental representation learning module.
Medical vision-language models (VLMs) have shown increasing potential for radiological image interpretation.
Extractive summary: sentences quoted from the sources.