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
@article{arxiv.2108.11154,
title = {Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation},
author = {Himashi Peiris and Zhaolin Chen and Gary Egan and Mehrtash Harandi},
journal= {arXiv preprint arXiv:2108.11154},
year = {2021}
}