StyleGAN2-based Out-of-Distribution Detection for Medical Imaging
McKell Woodland John Wood Caleb O'Connor Ankit B. Patel Kristy K. Brock
One barrier to the clinical deployment of deep learning-based models is the
presence of images at runtime that lie far outside the training distribution of
a given model. We aim to detect these out-of-distribution (OOD) images with a
generative adversarial network (GAN). Our training dataset was comprised of
3,234 liver-containing computed tomography (CT) scans from 456 patients. Our
OOD test data consisted of CT images of the brain, head and neck, lung, cervix,
and abnormal livers. A StyleGAN2-ADA architecture was employed to model the
training distribution. Images were reconstructed using backpropagation.
Reconstructions were evaluated using the Wasserstein distance, mean squared
error, and the structural similarity index measure. OOD detection was evaluated
with the area under the receiver operating characteristic curve (AUROC). Our
paradigm distinguished between liver and non-liver CT with greater than 90%
AUROC. It was also completely unable to reconstruct liver artifacts, such as
needles and ascites.