(Chest X-ray Analysis with Deep Learning)
(Information)
 
Line 30: Line 30:
 
* Supervision: CXR Team
 
* Supervision: CXR Team
 
* Location: Radboud University Medical Centre, Nijmegen, The Netherlands
 
* Location: Radboud University Medical Centre, Nijmegen, The Netherlands
* If you are interested in, please send your CV to erdi.calli (at) radboudumc.nl.
+
* If you are interested in, please contact your CV to Henkjan Huisman.

Latest revision as of 12:15, 10 September 2019

Chest X-ray Analysis with Deep Learning

Xray.jpg

Project description

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:

  • Can we detect the progression of diseases using multi time point exams?
  • Can we improve algorithms with unsupervised and semi-supervised deep learning?
  • Can we jointly train models on two chest x-rays (a frontal and lateral 2D projection image) and a corresponding chest CT scan of the same patient (a 3D image), and reconstruct 3D information from the 2D projections?

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.

Institute

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.

Tasks

  • Familiarize yourself with deep learning within DIAG.
  • Research and develop a DL model and algorithms for a specific task in Chest X-ray image analysis.
  • Write a scientific paper.

Requirements

  • Good grades
  • Working knowledge of deep learning and medical imaging
  • Writing skills

Information

  • Project duration: 6-12 months (can be tuned to your requirements)
  • Supervision: CXR Team
  • Location: Radboud University Medical Centre, Nijmegen, The Netherlands
  • If you are interested in, please contact your CV to Henkjan Huisman.