Certification of Deep Learning Models for Medical Image Segmentation
Othmane Laousy Alexandre Araujo Guillaume Chassagnon Nikos Paragios Marie-Pierre Revel Maria Vakalopoulou
In medical imaging, segmentation models have known a significant improvement
in the past decade and are now used daily in clinical practice. However,
similar to classification models, segmentation models are affected by
adversarial attacks. In a safety-critical field like healthcare, certifying
model predictions is of the utmost importance. Randomized smoothing has been
introduced lately and provides a framework to certify models and obtain
theoretical guarantees. In this paper, we present for the first time a
certified segmentation baseline for medical imaging based on randomized
smoothing and diffusion models. Our results show that leveraging the power of
denoising diffusion probabilistic models helps us overcome the limits of
randomized smoothing. We conduct extensive experiments on five public datasets
of chest X-rays, skin lesions, and colonoscopies, and empirically show that we
are able to maintain high certified Dice scores even for highly perturbed
images. Our work represents the first attempt to certify medical image
segmentation models, and we aspire for it to set a foundation for future
benchmarks in this crucial and largely uncharted area.