  Medical image data are usually imbalanced across different classes. One-class
classification has attracted increasing attention to address the data imbalance
problem by distinguishing the samples of the minority class from the majority
class. Previous methods generally aim to either learn a new feature space to
map training samples together or to fit training samples by autoencoder-like
models. These methods mainly focus on capturing either compact or descriptive
features, where the information of the samples of a given one class is not
sufficiently utilized. In this paper, we propose a novel deep learning-based
method to learn compact features by adding constraints on the bottleneck
features, and to preserve descriptive features by training an autoencoder at
the same time. Through jointly optimizing the constraining loss and the
autoencoder's reconstruction loss, our method can learn more relevant features
associated with the given class, making the majority and minority samples more
distinguishable. Experimental results on three clinical datasets (including the
MRI breast images, FFDM breast images and chest X-ray images) obtains
state-of-art performance compared to previous methods.
