Multi-view learning for bronchovascular pair detection

In many important image classification problems, acquiring class labels for training instances is costly, while gathering large quantities of unlabelled data is cheap. A semiautomated system for the classification of bronchovascular pairs based on co-training in high resolution computed tomography (HRCT) images is presented. A bronchovascular pair is formed between a bronchus and a vessel. The identification of such structures provides valuable diagnostic information in patients with suspected airway diseases. Co-training is a semi-supervised multi-view learning algorithm where classifiers trained with a small number of labelled examples are improved by augmenting the small training set with a large pool of unseen examples. We incorporate active learning where the user labels examples on which the two views disagree. The two views in our system are based on spatial relations and ERS, a gradient based feature set. In addition, the optimal parameters required in the pre-processing step before feature extraction and recognition was automatically chosen. The system was co-trained on 41 unlabelled HRCT scans selected from 26 patient studies. It was successfully evaluated on 26 other HRCT scans manually labelled in consultation with radiologists.