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Medical image data are usually imbalanced across different classes. One-classclassification has attracted increasing attention to address the data imbalanceproblem by distinguishing the samples of the minority class from the majorityclass. Previous methods generally aim to either learn a new feature space tomap training samples together or to fit training samples by autoencoder-likemodels. These methods mainly focus on capturing either compact or descriptivefeatures, where the information of the samples of a given one class is notsufficiently utilized. In this paper, we propose a novel deep learning-basedmethod to learn compact features by adding constraints on the bottleneckfeatures, and to preserve descriptive features by training an autoencoder atthe same time. Through jointly optimizing the constraining loss and theautoencoder's reconstruction loss, our method can learn more relevant featuresassociated with the given class, making the majority and minority samples moredistinguishable. Experimental results on three clinical datasets (including theMRI breast images, FFDM breast images and chest X-ray images) obtainsstate-of-art performance compared to previous methods.

Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images