PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

2 code implementations • 25 Dec 2023 • Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra, Hendrik Mattern, Gábor Janiga, Oliver Speck, Andreas Nürnberger, Sahani Pathiraja

In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets.

Decision Making Image Segmentation +3