Cross-adversarial local distribution regularization for semi-supervised
  medical image segmentation
Thanh Nguyen-Duc Trung Le Roland Bammer He Zhao Jianfei Cai Dinh Phung
Medical semi-supervised segmentation is a technique where a model is trained
to segment objects of interest in medical images with limited annotated data.
Existing semi-supervised segmentation methods are usually based on the
smoothness assumption. This assumption implies that the model output
distributions of two similar data samples are encouraged to be invariant. In
other words, the smoothness assumption states that similar samples (e.g.,
adding small perturbations to an image) should have similar outputs. In this
paper, we introduce a novel cross-adversarial local distribution (Cross-ALD)
regularization to further enhance the smoothness assumption for semi-supervised
medical image segmentation task. We conducted comprehensive experiments that
the Cross-ALD archives state-of-the-art performance against many recent methods
on the public LA and ACDC datasets.