Keywords: Medical imaging, out-of-distribution detection, chest X-ray, fundus, histology
Abstract: There is a rise in the use of deep learning for automated medical diagnosis, most notably in medical imaging. Such an automated system uses a set of images from a patient to diagnose whether they have a disease. However, systems trained for one particular domain of images cannot be expected to perform accurately on images of a different domain. These images should be filtered out by an Out-of-Distribution Detection (OoDD) method prior to diagnosis. This paper benchmarks popular OoDD methods in three domains of medical imaging: chest x-rays, fundus images and histology slides. Despite methods yielding good results on all three domains, they fail to recognize images close to the training distribution.
Track: full conference paper
TL;DR: We thoroughly benchmark out-of-distribution detection methods on multiple domains medical images.
Paper Type: well-validated application