Every year, about 24,000 women are referred by the national breast cancer screening program for further testing. A third of these women are referred because of only calcifications seen on the mammogram. Roughly 75% of women referred for calcifications do not have breast cancer. In the remaining 25% of women, breast cancer or a precursor thereof, namely ductal carcinoma in situ (DCIS), might be present. While DCIS is a precursor of breast cancer, current evidence shows that many DCIS diagnosis would remain indolent and would not cause any danger for the woman's life. Despite this, there is currently no way to distinguish between women whose DCIS will or will not develop into invasive breast cancer. Unfortunately, this means that thousands of women undergo hospital visits, or even surgery, radiotherapy, or systemic treatment they do not need. Therefore, most women are referred unnecessarily in retrospect, leading to unrest, uncertainty, and possibly overdiagnosis and overtreatment. 
We want to prevent that in the future with the help of artificial intelligence (AI). Using AI, we will address the problem at two levels: at the screening level where we use radiological data to determine which calcifications would not be a sign of breast cancer or DCIS and therefore, do not require a referral; and once referred for suspect DCIS, at the diagnostic level where we will build AI models that integrate the data from radiology, pathology, and molecular analyzes to determine which lesions will remain indolent and therefore do not require treatment. For this, we will need to develop deep learning techniques for detection, classification, and outcome prediction of calcifications and DCIS in multidisciplinary data. Additionally, we will design novel self-supervised contrastive learning techniques and combine these with model interpretability techniques to discover new knowledge about breast cancer. Ultimately, we want to make breast cancer screening even more targeted by optimizing the referral of women with calcifications and the subsequent decision-to-treat. 

The job description
As a PhD-candidate, you will be responsible for developing and evaluating state-of-the-art deep learning techniques in multidisciplinary data. Finally, you will validate these algorithms in independent cases to ensure the devised AI-algorithms' applicability in clinical practice.
You are embedded in the ICAI AI for Oncology Lab, a collaboration between the Netherlands Cancer Institute and the Informatics Institute of the University of Amsterdam. The lab's mission is to develop innovations in artificial intelligence for the improvement of diagnosis and therapy of cancer. You will discuss results with our team, publish your work in artificial intelligence / medical journals, and present it at international conferences. As part of the project, you will be collaborating with experts in breast radiology of the Radboud University Medical Center.
The daily supervisor in this project is Dr. J. Teuwen, lab manager of the ICAI AI for Oncology Lab. Other team members are the scientific directors Prof. Dr. C. Sanchez (UvA) and Prof. Dr. J-J. Sonke. The project's clinicians are Prof. Dr. J. Wesseling, breast pathologist (NKI), and Dr. R. Mann, breast radiologist (NKI / Radboudumc).

We are looking for an enthusiastic PhD student (or postdoc) who will develop novel deep learning algorithms and shape the research in collaboration with the project leaders, AI, and medical experts. 
We are looking for a motivated, goal-oriented, independent, and proactive PhD candidate or postdoc who is enthusiastic about working in a multidisciplinary setting. Preferably you have a master's degree in artificial intelligence, computer science, physics, mathematics, medicine, or equivalent. In any case, you should have experience with deep learning and have excellent programming skills. Your experience should be evident from the courses followed and your GitHub account.

Fixed-term contract: PhD: 4 years; postdoc: 3 years.

You will have the opportunity to follow high-quality courses offered by the OOA oncology graduate school throughout your PhD. The employment will be for a period of 4 years. The gross salary per month for our PhD will range from € 2.884,- to a maximum of € 3.322,- according to the standard PhD scales. The terms of employment will be in accordance with the CAO Ziekenhuizen (Collective Labor Agreement for Hospitals). 
Regarding the postdoc, the employment will be for a period of 3 years. The gross salary will range from € 3.447,- and € 4.077,- per month for a fulltime position, according to the FWG-function group 55 (postdoc), depending on previous experience as a postdoc. The terms of employment will be in accordance with the CAO Ziekenhuizen (Collective Labour Agreement for Hospitals). 
In addition you will receive a fixed end-of-year bonus in December (8,33%) and in May you will receive 8,33% holiday pay. For more information in regard to the secondary conditions please contact the recruiter via: c.de.santis@nki.nl.

Netherlands Cancer Institute

The Netherlands Cancer Institute comprises an internationally acclaimed research institute as well as a dedicated cancer clinic. This combination ensures rapid translation of basic research into clinical applications: today’s research for tomorrow’s cure.