Ongeveer 20 uur geleden - Universiteit van Amsterdam (UvA) - Amsterdam
The Van't Hoff Institute for Molecular Sciences (HIMS) and the Institute of Physics (IoP) of the Faculty of Science invite applications for a four-year …
One million men receive a prostate cancer diagnosis, and three hundred thousand die from prostate cancer each year worldwide. Magnetic Resonance Imaging (MRI) …
One million men receive a prostate cancer diagnosis, and three hundred thousand die from prostate cancer each year worldwide. Magnetic Resonance Imaging (MRI) represents a major breakthrough by accurately detecting clinically significant prostate cancers at an early, potentially more curable stage. The same MRI can also be used for better treatment targeting and for avoiding unnecessary systematic biopsies. MRI allows patient-specific guidance during surgery.
Radboudumc has recently set up an MRI in its Medical Innovation and Technology expert Center. Interpreting MRI imaging during surgery is however very challenging and time-consuming. Technology for real-time accurate modeling is not available. Artificial Intelligence and more specifically Deep Learning has shown promising results segmentation of medical images, mainly for diagnostic use. For interventional purposes, speed and accuracy are still major challenges.
This project is part of a recently awarded EDL perspectief project. EDL aims to make deep learning much more efficient. This PhD project aims to research deep learning that will demonstrably help clinicians to provide real-time modeling of MRI during an intervention. The research includes automatic segmentation using a massive MRI database. We closely work with clinicians in all projects and are collecting extensive data sets of expert annotations. State-of-the-art deep learning models will be continuously evaluated and implemented in a clinical prototype for validation and feedback. Results will be presented at scientific meetings and published in journals.
Radboudumc is a clinical expert on prostate MRI and technical expert in the field of prostate AI technology for over 20 years and an early adopter of deep learning in medical imaging. Deep learning is currently the most active research area within machine learning and computer vision, and medical image analysis. DIAG currently has 40 deep learning researchers focused on various medical image analysis topics. A team of scientific programmers is supporting our deep learning research, maintaining a large cluster of computers equipped with high-end GPUs for large-scale experimentation.
You will be part of a multidisciplinary team, consisting of machine learning and clinical researchers and collaborate with external partners. The main external partner for this project is Siemens Healthineers, but you will have opportunities to work with the other partners in the EDL project: AIIR Innovations, ASTRON, CWI, Cyclomedia, Cygnify, Donders Institute, FEI, 2getthere, GN Hearing, Holst Centre, ING, Intel, Irdeto, Lely, Mobiquity, NLeSC, NXP, NVIDIA, Océ, Schiphol, Scyfer, Sectra, Semiotic Labs, Sightcorp, Sorama, SURFsara, TASS International, Tata Steel, TU Dresden, Delft University of Technology, Eindhoven University of Technology, Thales, TNO, TomTom, University of Twente, University of Amsterdam, 3DUniversum, VicarVision, ViNotion, VU Amsterdam, Wageningen University & Research.
Positions can be filled by either a PhD student or postdoc.
You are a creative and ambitious researcher with a Ph.D. degree in Computer Science, Data Science, Physics, Engineering or Biomedical Sciences or similar, with a clear interest in machine learning, deep learning, medical image analysis. Good communication skills and expertise in software development, preferably in Python/C++, are essential. Experience with deep learning should be evident from the (online) courses you've followed, your publications, GitHub account, etc.