Robustness of Fine-Tuned CXR Foundation Models to Adversarial Perturbations

Abstract

Abstract: Radiologists play a crucial role in translating medical images into actionable reports. However, the field faces staffing shortages and increasing workloads. While automated approaches using vision-language models (VLMs) show promise as assistants, they require exceptionally high accuracy. Most current VLMs in radiology rely solely on supervised fine-tuning. Meanwhile, additional preference fine-tuning in the post-training pipeline has become standard practice in the general domain. The challenge in radiology lies in the prohibitive cost of obtaining radiologist feedback at scale. To address t

Research Question

What is the effect of fine-tuning CXR foundation models with human feedback on their robustness to adversarial perturbations, measured by comparing model confidence and accuracy on perturbed and original MIMIC-CXR/NIH-CXR14 test sets?

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Provenance