Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift

• URL: http://arxiv.org/abs/2006.10990v1
• Date: Fri, 19 Jun 2020 07:35:25 GMT
• Title: Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift
• Authors: Qinming Zhang, Luyan Liu, Kai Ma, Cheng Zhuo, Yefeng Zheng
• Abstract summary: We propose a novel cross-denoising framework using two peer networks to address domain shift and corrupted label problems. Specifically, each network performs as a mentor, mutually supervised to learn from reliable samples selected by the peer network to combat with corrupted labels. In addition, a noise-tolerant loss is proposed to encourage the network to capture the key location and filter the discrepancy under various noise-contaminant labels.
• Score: 28.940670115918728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep convolutional neural networks (DCNNs) have contributed many breakthroughs in segmentation tasks, especially in the field of medical imaging. However, \textit{domain shift} and \textit{corrupted annotations}, which are two common problems in medical imaging, dramatically degrade the performance of DCNNs in practice. In this paper, we propose a novel robust cross-denoising framework using two peer networks to address domain shift and corrupted label problems with a peer-review strategy. Specifically, each network performs as a mentor, mutually supervised to learn from reliable samples selected by the peer network to combat with corrupted labels. In addition, a noise-tolerant loss is proposed to encourage the network to capture the key location and filter the discrepancy under various noise-contaminant labels. To further reduce the accumulated error, we introduce a class-imbalanced cross learning using most confident predictions at the class-level. Experimental results on REFUGE and Drishti-GS datasets for optic disc (OD) and optic cup (OC) segmentation demonstrate the superior performance of our proposed approach to the state-of-the-art methods.

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