C$^3$PS: Context-aware Conditional Cross Pseudo Supervision for
  Semi-supervised Medical Image Segmentation
Peng Liu Guoyan Zheng
Semi-supervised learning (SSL) methods, which can leverage a large amount of
unlabeled data for improved performance, has attracted increasing attention
recently. In this paper, we introduce a novel Context-aware Conditional Cross
Pseudo Supervision method (referred as C$^3$PS) for semi-supervised medical
image segmentation. Unlike previously published Cross Pseudo Supervision (CPS)
works, this paper introduces a novel Conditional Cross Pseudo Supervision
(CCPS) mechanism where the cross pseudo supervision is conditioned on a given
class label. Context-awareness is further introduced in the CCPS to improve the
quality of pseudo-labels for cross pseudo supervision. The proposed method has
the additional advantage that in the later training stage, it can focus on the
learning of hard organs. Validated on two typical yet challenging medical image
segmentation tasks, our method demonstrates superior performance over the
state-of-the-art methods.