Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shifts and diagnose failure modes of biomedical vision models; this can be used in advance of deployment to assess readiness, potentially reducing cost and patient harm. Existing editing methods can produce undesirable changes, with spurious correlations learned due to the co-occurrence of disease and treatment interventions, limiting practical applicability. To address this, we train a text-to-image diffusion model on multiple chest X-ray datasets and introduce a new editing method RadEdit that uses multiple masks, if present, to constrain changes and ensure consistency in the edited images. We consider three types of dataset shifts: acquisition shift, manifestation shift, and population shift, and demonstrate that our approach can diagnose failures and quantify model robustness without additional data collection, complementing more qualitative tools for explainable AI.
@article{arxiv.2312.12865,
title = {RadEdit: stress-testing biomedical vision models via diffusion image editing},
author = {Fernando Pérez-García and Sam Bond-Taylor and Pedro P. Sanchez and Boris van Breugel and Daniel C. Castro and Harshita Sharma and Valentina Salvatelli and Maria T. A. Wetscherek and Hannah Richardson and Matthew P. Lungren and Aditya Nori and Javier Alvarez-Valle and Ozan Oktay and Maximilian Ilse},
journal= {arXiv preprint arXiv:2312.12865},
year = {2024}
}