PhD candidate ‘Development of explainable AI methods for end-to-end-learning with gigapixel images in the BigPicture consortium’

PhD candidate ‘Development of explainable AI methods for end-to-end-learning with gigapixel images in the BigPicture consortium’

Background

This project is part of the European BigPicture project, which aims to establish the largest digital pathology repository in the world, with over three million images and associated diagnostic information. This type of data offers incredible potential for machine learning algorithms to impact pathology practice, but also requires sophisticated AI tools to get the most from this data. That is where you come in. In this project you will develop new machine learning methods for end-to-end training on gigapixel-sized images, investigate AI explainability and transfer learning strategies, and work with algorithms for content-based image retrieval and federated learning. You will work together with the over forty partners in the consortium which spans almost the entirety of Europe (and even further), including both academic partners, small, and big corporations.

Profile

You are a creative and ambitious researcher with a 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.

Organization

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 the newest advances in machine learning with a focus on computer-aided diagnosis (CAD). Application areas include diagnostics and prognostics of breast, colon, prostate and lung cancer, but also non-oncological topics such as kidney transplantation. Our group is among the international front runners in the field, witnessed for instance by the highly successful Camelyon and Panda grand challenges which we organized.

Radboudumc

Radboud university medical center is a university medical center for patient care, scientific research, and education in Nijmegen. Radboud university medical center strives to be at the forefront of shaping the healthcare of the future. We do this in a person-centered and innovative way, and in close collaboration with our network. We want to have a significant impact on healthcare. We want to improve with each passing day, continuously working towards better healthcare, research, and education. And gaining a better understanding of how diseases arise and how we can prevent, treat, and cure them, day in and day out. This way, every patient always receives the best healthcare, now and in the future. Because that is why we do what we do.

Read more about our strategy and what working at Radboud university medical center means. Our colleagues would be happy to tell you about it. #weareradboudumc

Application

Please apply before Nov 22, 2021 via this link. Here you can also find more information on the application process and what needs to be included in your application.

People

Francesco Ciompi

Francesco Ciompi

Assistant Professor

Computational Pathology Group

Geert Litjens

Geert Litjens

Assistant Professor

Computational Pathology Group

Jeroen van der Laak

Jeroen van der Laak

Associate Professor

Computational Pathology Group