- 36 hours a week
- 4 years
- Date of publication: 29 November 2019
- Deadline: 3 January 2020
- Scale 10A: min € 2422 - max € 3103 gross per month at full employment (excl. vacation bonus and end of year payments)
- First interview scheduled: 10 January 2020
Job descriptionDo you want to contribute to the world’s first prospectively evaluated algorithm-supported workflow for digital pathology, which will increase the time of pathologists for complex diagnostics and reduce the wait time for patients?
Due to the tripling of skin cancer incidence over the past two decades, more skin biopsies and resections are performed than ever before. This has led to an enormous increase in workload for pathologists, who perform the microscopic diagnostics of skin samples. Machine learning and specifically deep learning offers a path to automating the diagnoses of skin samples, which would reduce the pressure on pathologists and the cost of diagnosis, both in time and money.
We are looking for a PhD candidate who is not just interested in developing an algorithm which can perform skin diagnostics at the level of an expert pathologist, but also explicitly wants to identify the most fruitful way of integrating these algorithms into the routine workflow.
The PhD candidate will develop algorithms for segmentation of different skin tissue classes, subtyping of basal cell carcinoma, and identification of rare incidental findings. Subsequently, the candidate will focus on the development and prospective evaluation of the optimal algorithm-integrated workflow in a real world clinical setting. After completion, this will result in the world’s first prospectively evaluated algorithm-supported workflow for digital pathology which will free up pathologists’ time for more complex diagnostics, and reduce the wait time for patients.
The research should result in a Ph.D. thesis.
Tasks and responsibilities
Within this project you will:
- Develop and validate deep learning algorithms to identify varying types of histopathologic skin diseases such as basal cell carcinoma.
- Identify the best strategies to use such algorithms in clinical practice in close collaboration with our pathologists.
- Implement this strategy in clinical workflow with the aid of our industrial partner, Sectra.
- Evaluate the use of the algorithm in real-world clinical practice.
ProfileYou are a creative and ambitious researcher with an MSc degree in Computer Science, Data Science, Engineering, Technical Medicine, Biomedical Sciences or similar, with a clear interest in artificial intelligence and medical image analysis. Good communication and organizational skills are essential. Experience with deep learning and programming, preferably in Python, are a plus and should be evident from the (online) courses you've followed, your publications, GitHub account, etc.
OrganizationThe Computational Pathology Group
The Computational Pathology Group is a research group of the department of Pathology of the Radboud University Medical Center (Radboudumc). We are also part of the cross-departmental Diagnostic Image Analysis Group (DIAG) at Radboudumc, with researchers in the departments of Radiology and Nuclear Medicine, Pathology and Ophthalmology. We develop, validate and deploy novel medical image analysis methods, usually based on deep learning technology and focusing on computer-aided diagnosis (CAD).
Application areas include diagnostics and prognostics of breast, colon, prostate and lung cancer. Our group is among the international front runners in the field, witnessed for instance by the highly successful CAMELYON16 and CAMELYON17 grand challenges which we organized. Within this project we will also partner with other medical centers such as the Utrecht University Medical Center.
Radboudumc strives to be a leading developer of sustainable, innovative and affordable healthcare to improve the health and wellbeing of people and society in the Netherlands and beyond. This is the core of our mission: To have a significant impact on healthcare. To get a better picture of what this entails, check out our strategy.
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Employment conditionsStarting date is March 1, 2020.
Upon commencement of employment we require a certificate of conduct (Verklaring Omtrent het Gedrag, VOG) and there will be, depending on the type of job, a screening based on the provided cv. Radboud university medical center’s HR Department will apply for this certificate on your behalf.
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Comments and contact informationAll additional information about the vacancy can be obtained from Dr. ir. Geert Litjens, assistant professor. Use the Apply button to submit your application.
Please apply before January 3.
Recruitment agencies are asked not to respond to this job posting.
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