  Convolutional neural networks demonstrated outstanding empirical results in
computer vision and speech recognition tasks where labeled training data is
abundant. In medical imaging, there is a huge variety of possible imaging
modalities and contrasts, where annotated data is usually very scarce. We
present two approaches to deal with this challenge. A network pretrained in a
different domain with abundant data is used as a feature extractor, while a
subsequent classifier is trained on a small target dataset; and a deep
architecture trained with heavy augmentation and equipped with sophisticated
regularization methods. We test the approaches on a corpus of X-ray images to
design an anatomy detection system.
