Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2108.11154v1
• Date: Wed, 25 Aug 2021 10:16:12 GMT
• Title: Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation
• Authors: Himashi Peiris, Zhaolin Chen, Gary Egan, Mehrtash Harandi
• Abstract summary: We propose a semi-supervised image segmentation technique based on the concept of multi-view learning. Our proposed method outperforms state-of-the-art medical image segmentation algorithms consistently and comfortably.
• Score: 14.535295064959746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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