Beyond Target Scores: Measuring Off-Target Drift in Diffusion-Based Medical Image Editing
Quick Answer
The study introduces CIB-Med-1, a benchmark for evaluating off-target drift in medical image editing, revealing that diffusion models can inadvertently alter non-target findings.
Quick Take
A constrained diffusion guidance approach shows improved target progression while significantly reducing off-target drift, demonstrating the need for trajectory-level evaluation in medical imaging.
Key Points
- CIB-Med-1 benchmarks directional pleural effusion editing in chest radiography.
- Constrained diffusion guidance reduces median off-target drift from 0.46 to 0.20.
- Human validation shows stronger agreement with intended progression orderings at τ=0.61.
- Standard evaluation metrics may overlook significant off-target changes in medical imaging.
- The study emphasizes trajectory-level semantic control over endpoint score maximization.
Paper Resources
Source Excerpt
Diffusion models can now edit medical images in visually plausible ways, but the standard evaluation question is too narrow: did the target score increase? In clinical imaging, target findings are entangled with co-morbidities, acquisition effects, and selection bias, so a model can appear successful by changing correlated non-target findings rather than isolating the intended pathology. We introduce CIB-Med-1, a trajectory-level benchmark for controlled biomarker editing in chest radiography. C
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