Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation
This addresses the problem of reducing annotation costs for medical professionals, though it appears incremental as it builds on multi-view learning concepts.
The paper tackles the challenge of medical image segmentation with limited annotated data by proposing a semi-supervised technique using adversarial dual-view training, and it outperforms state-of-the-art methods consistently across several datasets.
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