  Segmentation of images is a long-standing challenge in medical AI. This is
mainly due to the fact that training a neural network to perform image
segmentation requires a significant number of pixel-level annotated data, which
is often unavailable. To address this issue, we propose a semi-supervised image
segmentation technique based on the concept of multi-view learning. In contrast
to the previous art, we introduce an adversarial form of dual-view training and
employ a critic to formulate the learning problem in multi-view training as a
min-max problem. Thorough quantitative and qualitative evaluations on several
datasets indicate that our proposed method outperforms state-of-the-art medical
image segmentation algorithms consistently and comfortably. The code is
publicly available at https://github.com/himashi92/Duo-SegNet
