SEMI-SUPERVISED ROBUST ONE-CLASS CLASSIFICATION IN RKHS FOR ABNORMALITY DETECTION IN MEDICAL IMAGES

Abnormality detection in medical images is a one-class classification problem for which typical methods use variants of kernel principal component analysis or one-class support vector machines. However, in practical deployment scenarios, many such methods are sensitive to the outliers present in the imperfectly-curated training sets. Current robust methods use heuristics for model fitting or lack formulations to leverage even a small amount of high-quality expert feedback. In contrast, we propose a novel method combining (i) robust statistical modeling, extending the multivariate generalized-Gaussian to a reproducing kernel Hilbert space, with (ii) semi-supervised learning to leverage a small expert-labeled outlier set. Results on simulated and real-world data, including endoscopy data, show that our method outperforms the state of the art in accurately detecting abnormalities.