PhD student medical imaging and radiomics. (KU Leuven Global PhD Partnerships 2020 joint PhD Position with UM)

Updated: 4 months ago
Deadline: 13 Sep 2020

PhD student medical imaging and radiomics. (KU Leuven Global PhD Partnerships 2020 joint PhD Position with UM)
PhD student medical imaging and radiomics. (KU Leuven Global PhD Partnerships 2020 joint PhD Position with UM)
Published Deadline Location
28 Aug 13 Sep Maastricht

We are looking for a highly motivated and talented PhD student to develop deep learning and radiomics techniques for contrast enhanced mammography (CEM) and validate these techniques using synthetic data, to support the detection and diagnosis of (less common) breast cancer subtypes.
Job description

The difficulty of detecting or characterizing less common breast cancer subtypes from medical images has long been recognized. The combination of advances in X-ray imaging and artificial intelligence (AI) open up new avenues for this problem. This project will build and validate a combined deep learning and handcrafted radiomics solution for CEM. The new tool will offer decision support for detection and characterization using the quantitative perfusion patterns uncovered by CEM.

The large number of training images required for building and testing AI models is challenging for a relatively new technique such as CEM, where large clinical trials are still absent. This data poverty will be overcome with the creation of an abundant amount of synthetic, virtual cases, including in particular difficult to detect and less common cancer subtypes.

In parallel, eReaders (or model observers) will be tuned to simulate human reader performance. This is a new approach to create relevant ground truth data. Ultimately, it will allow the execution of virtual clinical trials to investigate the clinical impact and cost-effectiveness of using the AI model in the radiological practice.

Our ultimate goal is to offer decision support for detection and characterization of breast lesions. We believe that developing AI models, based partially on synthetic data and tested in virtual clinical trials will support this ultimate goal. In this research project, we investigate the following hypotheses:

  • An AI model can improve the detection and characterization of (less common) breast lesions.
  • Synthetic images can reflect real images to a high extent and have comparable imaging features. 
  • Virtual clinical trials can be used to assess the clinical value and cost-effectiveness of AI models.
  • This project is a collaboration between the Department of Imaging and Pathology of Leuven University (KUL) and the Department of Precision Medicine of Maastricht University (UM).

    During the first two years the PhD student will be employed at KU Leuven. The Primary Investigator of the project, Hilde Bosmans (Scopus Hirsch-index 45, 243 peer reviewed publications), is professor in both the faculty of medicine and the faculty of sciences of the KUL and in the University of Liège, Belgium. Together with her team, she performs the medical radiation physics services in the University Hospital and in several Flemish hospitals, and for a network of 102 mammography units of the Flemish breast cancer screening. A large part of her activities is therefore devoted to breast cancer imaging techniques, and in particular to the development of new and better quality assurance protocols. They have developed the concept of virtual clinical trials to study several aspects of the medical imaging chain, such as the impact of image processing on detectability of lesions. In addition, prof. Nicholas Marshall, medical physicist, Lesley Cockmartin, PhD, biomedical scientist, and prof. Chantal Van Ongeval, breast radiologist, will be involved in supervision of the student with regard to synthetic data creation and clinical correctness and relevance.

    During year 3 and 4 of the PhD trajectory, the PhD student will be employed at Maastricht University in the Dpt of Precision Medicine. Prof. Philippe Lambin (ERC advanced & ERC PoC grant laureate, 490 peer reviewed scientific papers, Hirsch Index: 94) co-invented the concept of radiomics (three Nature papers published on the concept, and several of his papers on this topic are cited over 500 times, 2> 1000 times). The group excels at applying machine learning methods such as deep learning & handcrafted radiomics on vast amounts of medical data. Cary Oberije, PhD, senior clinical data scientist, has extensive experience in designing and analysing clinical studies and is responsible for daily supervision of the student. There will be a close collaboration with Dr. Marc Lobbes, MD, PhD, who is breast radiologist in the University Hospital. He is co-PI on this project for the University of Maastricht and world specialist for CEM. He shares the clinical supervision of the project with prof. Chantal Van Ongeval. He will provide all necessary radiological information and experience regarding CEM and studies using CEM. In addition, Bram Ramaekers, PhD, senior researcher on health economics, will be involved on behalf of Maastricht university to guide the health economical aspects.

    • max. 38 hours per week
    • €2395—€3061 per month
    • Maastricht View on Google Maps

    Maastricht University (UM)


    The applicant has completed a Master in (bio)medical engineering, technical medicine, physics, machine learning, computer science, biomedical sciences or equivalent, with an interest for quantitative imaging, Deep Learning, radiomics and synthetic data.   

    We are looking for a scientist with a positive attitude, motivated to learn new approaches and ready to work hard to build a scientific career. The candidate should have a sociable personality with good communication skills, a problem-solving attitude, learn fast to plan his own workload effectively and to delegate when necessary and have conceptual ability.

    Additional requirements: 

    • Programming experience preferably a scripting language such as Python, R or Matlab
    • Strong interest to unravel the clinical relevance and (bio)medical background of the obtained results
    • Able to communicate adequately to different target audiences in the interdisciplinary team (medical physicists, radiologists, biomedical researchers, data scientists, ....)
    • Fluent in English, both writing and speaking
    • Preferably experience with modelling techniques/machine learning techniques
    • Preferably experience with imaging techniques (CT, MRI, PET), DICOM and image analysis
    • Interest in clinical trial methodologies and virtual trials
    • Interest in assessment of health economic aspects of (bio)medical applications
    • An independent and practical personality. You are able to take initiatives.
    • You will be working at the University of Leuven (KUL) as well as at the campus Maastricht ( ). You show the necessary flexibility in your working location.

    Conditions of employment

    Fixed-term contract: 4 years.

    You will be appointed and paid as PhD student. We offer full-time employment for a PhD-researcher in an international PhD training program taught in English. The appointment will be for a period of 2 years and will be extended for another 2 years after positive evaluation.

    The terms of employment of Maastricht University are set out in the Collective Labour Agreement of Dutch Universities (CAO). Furthermore, local UM provisions also apply. For more information look at the website > Support > UM employees.


    GROW-School for Oncology and Developmental Biology

    The School for Oncology & Developmental Biology (GROW) focuses on research and teaching of genetic and cellular mechanisms, as well as environmental and life-style factors that underlie normal (embryonic and fetal) and abnormal (cancer) development. The emphasis is on basic and translational research, aiming at innovative approaches for individualizing prevention, patient diagnosis, and treatment for genetically determined diseases and cancer.

    Additional information

    Additional information can be obtained from Prof. Hilde Bosmans ( and dr. Cary Oberije ( ).


    Application procedure

    The selection procedure consists of a preselection based on an application file and an interview. Please attach a motivation letter to your application file. 
    You can send your application to:
    Please mention also the vacancy number/Job number (AT2020.258) and the project name in your application.
    UM and KUL are committed to nurturing an inclusive culture and a welcoming atmosphere. This inclusiveness strategy has resulted in a very diverse representation of nationalities and cultures. We strongly believe that diversity (including, but not limited to nationality, age and gender) of the staff and student population will increase the quality of KUL & UM education & research. Fostering diversity and inclusivity creates an academic community where individual talents thrive, and values and differences are cherished. We strongly encourage you to apply if you are qualified for this position.


    Apply via postal mail
    Apply via postal mail

    Don't forget to mention AcademicTransfer and the job number: AT2020.258 in your letter.

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