Automatic Classification of MRI Contrasts Using a Deep Siamese Network and One-Shot Learning

Fully automatic classification of magnetic resonance (MR) brain images into different contrasts is desirable for facilitating image processing pipelines, as well as for indexing and retrieving from medical image archives. In this paper, we present an approach based on a Siamese neural network to learn a discriminative feature representation for MR contrast classification. The proposed method is shown to outperform a traditional deep convolutional neural network method and a template matching method in identifying five different MR contrasts of input brain volumes with a variety of pathologies, achieving 98.59% accuracy. In addition, our approach permits one-shot learning, which allows generalization to new classes not seen in the training set with only one example of each new class. We demonstrate accurate one-shot learning performance on a sixth MR contrast that was not included in the original training.