ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation

Understand

We propose adversarial constrained-CNN loss, a new paradigm of constrained-CNN loss methods, for weakly supervised medical image segmentation.

• In the new paradigm, prior knowledge is encoded and depicted by reference masks, and is further employed to impose constraints on segmentation outputs through adversarial learning with reference masks.
• Unlike pseudo label methods for weakly supervised segmentation, such reference masks are used to train a discriminator rather than a segmentation network, and thus are not required to be paired with specific images.
• Our new paradigm not only greatly facilitates imposing prior knowledge on network's outputs, but also provides stronger and higher-order constraints, i.e., distribution approximation, through adversarial learning.