Stenosis Detection with Deep Convolutional Neural Networks

Recent popularity of deep learning methods inspires to find new applications for them. One of promising areas is medical diagnosis support, especially analysis of medical images. In this paper we explore the possibility of using Deep Convolutional Neural Networks (DCNN) for detection of stenoses in angiographic images. One of the biggest difficulties is a need for large amounts of labelled data required to properly train deep model. We demonstrate how to overcome this difficulty by using generative model producing artificial data. Test results shows that DCNN trained on artificial data and fine-tuned using real samples can achieve up to 90% accuracy, exceeding results obtained by both traditional, feed-forward networks and networks trained using real data only