Overview
SOUSA is built for realistic medical imaging workflows where dense expert annotation is scarce. The framework combines sparse scribble supervision with large pools of unlabeled data in a teacher-student setting, reducing the annotation burden required for segmentation models to become useful.
Within the broader imaging direction, SOUSA serves as a data-efficiency method: a way to make segmentation pipelines trainable when high-quality labels are expensive or slow to obtain.