PhD Student in Semi-supervised Learning for Medical Image Analysis

Project description
Most recent successes of machine learning have been based on Supervised Learning (SL) methods, fueled by large quantities of parallel compute power and humanly annotated training data. However, that option quickly becomes intractable due to the labour intensive work of manual annotation, especially for medical image data. Instead, many believe that Semi-Supervised Learning (SSL) will drive the next AI revolution by using vast amount of unlabeled data (and some labeled examples) to discover all concepts and underlying causes that matter when interpreting an image. In this project, we will develop new methods and techniques for SSL and apply it to medically relevant problems where lots of image data is available.

We will work with different datasets and applications. Here we describe two medical image domains of interest. The first one is based on the SCAPIS study, a population study with collected CT examinations of over 30,000 individuals. These examinations can be used to analyze, for instance, possible atherosclerosis in the coronary arteries, which in turn can predict the risk of myocardial infarction in the future. The second problem concerns automatic analysis and diagnosis of cardiac ultrasound images. Currently, there are 90.000 of so called echocardiographies collected. A large and continously growing dataset is linked to the images resulting in a comprehensive characterization of the individuals. These data can be combined with image data for SSL applications and for a deeper understanding of the disease process. The research will be performed in close collaboration with medical researchers from Sahlgrenska Academy at Gothenburg University.

Information about the research groups
The position will be jointly supervised with one professor from the Computer Vision Group and one from the Signal Processing Group, both at the department of Electrical Engineering. The Computer Vision Group conducts research in the field of automatic image interpretation and targets both medical applications, such as the development of new and more effective methods for analysis, support and diagnostics, as well as general computer vision applications including autonomously guided vehicles, image-based localization, and object recognition. The main research problems include mathematical theory, algorithms and machine learning (deep learning) for inverse problems in artifical intelligence. The Signal Processing group conducts research in the field of physical and statistical signal and image modeling and inference. We actively pursue research in target tracking, array signal processing, estimation, detection and machine learning. Projects range from development of mathematical theory, method development and applications in the area of perception for autonomous vehicles, radar systems and biomedical devices.

Major responsibilities
Your major responsibilities are to pursue your own doctoral studies. You are expected to develop your own scientific concepts and communicate the results of your research verbally and in writing, both in Swedish and in English. The position generally also includes teaching on Chalmers' undergraduate level or performing other duties corresponding to 20 per cent of working hours.

Contract terms
Full-time temporary employment. The position is limited to a maximum of five years.

Qualifications
To qualify as a PhD student, you must have a master's level degree corresponding to at least 240 higher education credits in a relevant field (physics, mathematics or computer science).

The position requires sound verbal and written communication skills in English. Swedish is not a requirement but Chalmers offers Swedish courses.

Chalmers continuously strives to be an attractive employer. Equality and diversity are substantial foundations in all activities at Chalmers.

Our offer to you
Chalmers offers a cultivating and inspiring working environment in the dynamic city of Gothenburg. 
Read more about working at Chalmers and our benefits for employees.

Application procedure
To apply, please go to: https://www.chalmers.se/en/about-chalmers/Working-at-Chalmers/Vacancies/Pages/default.aspx?rmpage=job&rmjob=9382

Application deadline: 11 April, 2021