PhD Position F/M Effective AI based Characterization of Prostate Cancer from Multiparametric MRI

The Inria center at Université Côte d'Azur includes 42 research teams and 9 support services. The center’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regional economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur  is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

This PhD is part of the French national AICOO project, which brings together several innovative companies and both public and private clinical centers.

The AICOO project focuses in particular on the diagnosis of prostate cancer which is one of the most common cancers affecting men in developed countries. Multiparametric Magnetic Resonance Imaging (mp-MRI), which involves the combined analysis of multiple MR sequences, is now regarded as the most accurate imaging method for detecting prostate cancer. However, a significant barrier to its widespread use as a diagnostic tool is the extensive training required for radiologists to perform accurate analyses.

This challenge has driven the development of decision support systems to assist radiologists in detecting, characterizing, and monitoring prostate cancer lesions. For several years, computer scientists from Inria and radiologists from Paris Hospitals have been collaborating to address this issue, notably by creating two large, high-quality databases of mp-MR images.

The goal of this thesis is to develop advanced machine learning solutions for the automatic characterization of prostate lesion malignancy using multiparametric Magnetic Resonance Imaging. This will involve evaluating the image quality required to achieve accurate predictions from AI algorithms and calibrating the confidence levels of those predictions.

Additionally, the thesis will investigate the integration of specific clinical information with mp-MRI data to enhance algorithm performance. Ultimately, the research aims to provide interpretable image analysis to ensure that the results are reliable and accessible for practitioners.

Main activities (5 maximum) : Propose new methods to characterize prostate lesions from MR images, Evaluate those methods on project specific databases, write scientific papers, present the works’ progress to partners.

• Master degree with strong competences in statistical learning and mathematical modeling, as well as knowledge in medical imaging, signal and image processing (Master 2 level).
• Solid programming and IT skills are necessary (Python and C++, bash scripting, version control systems).
• Strong communication abilities with fluent English (written and spoken)

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Contribution to mutual insurance (subject to conditions)

Gross Salary per month: 2100€ brut per month (year 1 & 2) and 2190€ brut per month (year 3)