A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation

• URL: http://arxiv.org/abs/2004.07995v1
• Date: Thu, 16 Apr 2020 23:41:50 GMT
• Title: A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
• Authors: Ruizhe Li, Dorothee Auer, Christian Wagner, Xin Chen
• Abstract summary: We propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN) Our method is able to significantly improve beyond fully supervised model learning by incorporating unlabeled data.
• Score: 7.141405427125369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning based image segmentation has achieved the state-of-the-art performance in many medical applications such as lesion quantification, organ detection, etc. However, most of the methods rely on supervised learning, which require a large set of high-quality labeled data. Data annotation is generally an extremely time-consuming process. To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN). An encoder-decoder based DCNN is initially trained using a few annotated training samples. This initially trained model is then copied into sub-models and improved iteratively using random subsets of unlabeled data with pseudo labels generated from models trained in the previous iteration. The number of sub-models is gradually decreased to one in the final iteration. We evaluate the proposed method on a public grand-challenge dataset for skin lesion segmentation. Our method is able to significantly improve beyond fully supervised model learning by incorporating unlabeled data.

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