  With the advents of deep learning, improved image classification with complex
discriminative models has been made possible. However, such deep models with
increased complexity require a huge set of labeled samples to generalize the
training. Such classification models can easily overfit when applied for
medical images because of limited training data, which is a common problem in
the field of medical image analysis. This paper proposes and investigates a
reinforced classifier for improving the generalization under a few available
training data. Partially following the idea of reinforcement learning, the
proposed classifier uses a generalization-feedback from a subset of the
training data to update its parameter instead of only using the conventional
cross-entropy loss about the training data. We evaluate the improvement of the
proposed classifier by applying it on three different classification problems
against the standard deep classifiers equipped with existing
overfitting-prevention techniques. Besides an overall improvement in
classification performance, the proposed classifier showed remarkable
characteristics of generalized learning, which can have great potential in
medical classification tasks.
