Boundary and Distance Map
(***) Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation, MICCAI 2019, paper
Problem
- In representation learning, auxiliary-tasks are often designed to leverage free-of-cost supervision which is derived from existing target labels
- The purpose of including auxiliary tasks is not only to learn a shared representation but also to learn efficiently by solving the common meta-objective
Contribuction
- We introduce two complementary-tasks in context of organ-specific shapeprior learning. We show that the inclusion of these complementary-tasks alongside the segmentation task improves its overall performance.
Result and Conclusion
- In medical image segmentation where large data sets are scarce and corresponding dense annotation is expensive, designing complementary-task by leveraging existing target label could be beneficial to learn a generalized representation