Abnormality Detection for Medical Images UsingSelf-Supervision and Negative Samples

Recent progress in computer-aided technologies has had a considerable impact on helping experts with a reliable and fast diagnosis of abnormal samples. In particular, self-supervised and self-distillation techniques have advanced automated out-of-distribution (OOD) detection in the image domain. Further improvements in OOD detection have been observed by including negative samples derived from shifting transformations of real images. In this work, we study different ways of creating negative samples for medical images and how effective they are when leveraging them in a self-supervised self-distillation framework. We investigate the impact of various types of negative examples by applying different shifting transformations on samples when they are derived from in-distribution training data, from an auxiliary dataset, or a combination of both. For the case of the auxiliary dataset, we compare the OOD detection performance when auxiliary samples are extracted from an indomain or an out-domain. Our approach uses only data belonging to healthy people during the training procedure and does not require any additional information from labels. We demonstrate the efficiency of our technique by comparing abnormality detection performance on diverse medical datasets, setting new benchmarks for pneumonia, polyp, and glaucoma detection from X-ray, colonoscopy, and ophthalmology images.