Reinforcing Medical Image Classifier to Improve Generalization on Small Datasets
This addresses the common issue of limited training data in medical image analysis, though it appears incremental as it builds on existing overfitting-prevention techniques.
The paper tackles the problem of deep learning models overfitting on small medical image datasets by proposing a reinforced classifier that uses generalization feedback from a subset of training data to update parameters, showing overall improvement in classification performance across three different problems.
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.