Beyond Target Scores: Measuring Off-Target Drift in Diffusion-Based Medical Image Editing
Researchers introduce CIB-Med-1, a new benchmark for evaluating diffusion-based medical image editors, revealing that these models often increase target pathology scores by unintentionally altering correlated non-target findings. The study also proposes a constrained diffusion guidance method that reduces off-target semantic drift while maintaining effective target editing, as demonstrated on chest radiographs.
Why it matters: This work exposes a critical failure mode in medical image editing and provides a practical framework for evaluating and improving semantic control, which is essential for safe clinical use.
Full story at: arXiv Computer Vision ↗