Unsupervised Medical Image Segmentation

Medical image segmentation is a relevant task as it serves as the first step for several diagnosis processes, thus it is indispensable in clinical usage. Whilst major success has been reported using supervised techniques, they assume a large and well-representative labelled set. This is a strong assumption in the medical domain where annotations are expensive, time-consuming, and inherent to human bias. To address this problem, unsupervised techniques have been proposed in the literature yet it is still an open problem due to the difficulty of learning any transformation pattern. In this project, we propose to investigate unsupervised medical image segmentation via registration. The core of our approach is to exploit image-level registration to perform unsupervised medical image segmentation.

Who's involved


If you're looking to do a #PhD, @blood_counts is an exciting new project to be part of 👇 https://t.co/RE3pmfIE9F View on Twitter