Semi-supervised Learning for Image Modality Classification

Searching for medical image content is a regular task for many physicians, especially in radiology. Retrieval of medical images from the scientific literature can benefit from automatic modality classification to focus the search and filter out non-relevant items. Training datasets are often unevenly distributed regarding the classes resulting sometimes in a less than optimal classification performance. This article proposes a semi-supervised learning approach applied using a k-Nearest Neighbours (k-NN) classifier to exploit unlabelled data and to expand the training set. The algorithmic implementation is described and the method is evaluated on the ImageCLEFmed modality classification benchmark. Results show that this approach achieves an improved performance over supervised k-NN and Random Forest classifiers. Moreover, medical case-based retrieval also obtains higher performance when using the classified modalities as filter. This shows that image types can be classified well using visual information and they can then be used in a variety of applciations.