We are in the middle of a revolution of Artificial Intelligence, and deep learning in particular, that is rapidly changing the field of medical image analysis. Computers are now able to do things that were not considered possible a few years ago. This project focuses on the most common radiological exam: the chest x-ray. Hundreds of millions of such images are acquired in hospitals and clinics all over the world, for a large number of indications. In our group, we are developing algorithms in order to automatically find and quantify a broad spectrum of diseases and abnormal signs in chest radiographs. We use publicly available datasets, such as ChestXray14 (112,120 exams), MIMIC-CXR (371,920 exams), CheXpert (224,316 exams), and PadChest(160,868 exams). We also have collected our own dataset, consisting of hundreds of thousands of image exams, and are working with a network of partner sites to extend and annotate our databases. Research questions include, but are not limited to:
We collaborate with various companies (Delft Imaging Systems, Thirona, and Smart Reporting) and we expect that the results of the project will be integrated in products, both aimed at the Western market and developing countries.
The work will be executed in the Diagnostic Image Analysis Group (DIAG) of the Radboud University Nijmegen Medical Centre. DIAG is a leading research group in computer-aided detection and diagnosis (CAD). You will be supervised by the Chest X-ray team.