M. Murrow

CARE-MVLM: Counterfactual Abstention and Region-Grounded Evidence in Mammography Vision-Language Models

From Clinical Free Text to Auditable Concepts: An Agentic Framework for Interpretable Prediction

An agentic workflow that takes a prediction task and a raw text corpus as input and produces an auditable feature layer that achieves predictive performance comparable to direct BioClinicalBERT prediction on both tasks while additionally providing explicit, interpretable, and traceable task-specific concepts.

Accuracy Overstates Evidence Grounding and Abstention Reliability in Mammography Vision-Language Models

An evidence-grounded selective evaluation benchmark that evaluates Pathology classification and Abnormality identification together with label-aware lesion localization suggests that answer accuracy and abstention behavior can substantially overstate the reliability of current VLM when predictions are not verified agai...