2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-2.4
Paper Title SEMI-SUPERVISED MEDICAL IMAGE SEMANTIC SEGMENTATION WITH MULTI-SCALE GRAPH CUT LOSS
Authors Junxiao Sun, Yan Zhang, Southeast University, China; Jian Zhu, Shandong Academy of Medical Sciences, China; Jiasong Wu, Youyong Kong, Southeast University, China
SessionMLR-APPL-IP-2: Machine learning for image processing 2
LocationArea E
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
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Abstract Most semantic segmentation methods are based on supervised convolutional neural networks which require large amounts of labeled data. However, the acquisition of a large number of high-quality labels is time-consuming and of high annotation cost for medical images. In this paper, we propose a semi-supervised learning framework based on a novel multi-scale graph cut loss function. Firstly, the multi-scale features obtained from the segmentation network are utilized to construct the graph in non-Euclidean space. Then the long-distance information between voxels at different scales can be captured through the graph embedding module. After that, the graph cut loss is calculated according to the final latent features. Only a few labeled data is needed in our proposed method, which is of significance in the practical clinic. The experiments on the BrainWeb20 dataset and the IBSR18 dataset demonstrate the effectiveness of the proposed method compared to the well-known state-of-the-art methods.