Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision

Understand

Deep learning has achieved remarkable success in medicalimage segmentation, but it usually requires a large numberof images labeled with fine-grained segmentation masks, andthe annotation of these masks can be very expensive andtime-consuming.

• Therefore, recent methods try to use un-supervised domain adaptation (UDA) methods to borrow in-formation from labeled data from other datasets (source do-mains) to a new dataset (target domain).
• However, due tothe absence of labels in the target domain, the performance ofUDA methods is much worse than that of the fully supervisedmethod.
• In this paper, we propose a weakly supervised do-main adaptation setting, in which we can partially label newdatasets with bounding boxes, which are easier and cheaperto obtain than segmentation masks.