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

by   Himashi Peiris, et al.
Monash University

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


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Code Repositories


[MICCAI2021] This is an official PyTorch implementation for "Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation"

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