CUTS: A Fully Unsupervised Framework for Medical Image Segmentation. (arXiv:2209.11359v1 [cs.CV])

In this work we introduce CUTS (Contrastive and Unsupervised Training for
Segmentation) the first fully unsupervised deep learning framework for medical
image segmentation, facilitating the use of the vast majority of imaging data
that is not labeled or annotated. Segmenting medical images into regions of
interest is a critical task for facilitating both patient diagnoses and
quantitative research. A major limiting factor in this segmentation is the lack
of labeled data, as getting expert annotations for each new set of …

arxiv framework image medical segmentation unsupervised