  The application of Deep Learning (DL) for medical diagnosis is often hampered
by two problems. First, the amount of training data may be scarce, as it is
limited by the number of patients who have acquired the condition to be
diagnosed. Second, the training data may be corrupted by various types of
noise. Here, we study the problem of brain tumor detection from magnetic
resonance spectroscopy (MRS) data, where both types of problems are prominent.
To overcome these challenges, we propose a new method for training a deep
neural network that distills particularly representative training examples and
augments the training data by mixing these samples from one class with those
from the same and other classes to create additional training samples. We
demonstrate that this technique substantially improves performance, allowing
our method to reach human-expert-level accuracy with just a few thousand
training examples. Interestingly, the network learns to rely on features of the
data that are usually ignored by human experts, suggesting new directions for
future research.
