  This study investigates weakly supervised image segmentation using loose
bounding box supervision. It presents a multiple instance learning strategy
based on polar transformation to assist image segmentation when loose bounding
boxes are employed as supervision. In this strategy, weighted smooth maximum
approximation is introduced to incorporate the observation that pixels closer
to the origin of the polar transformation are more likely to belong to the
object in the bounding box. The proposed approach was evaluated on a public
medical dataset using Dice coefficient. The results demonstrate its superior
performance. The codes are available at
\url{https://github.com/wangjuan313/wsis-polartransform}.
