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DNetUnet: a semi-supervised CNN of medical image segmentation for super-computing AI service

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Abstract

Deep learning approaches have achieved good performance in segmenting medical images. In this paper, we propose a new convolutional neural network architecture named DNetUnet, which combines U-Nets with different down-sampling levels and a new dense block as feature extractor. In addition, DNetUnet is a semi-supervised learning method, which can be used not only to obtain expert knowledge from the labelled corpus, but also to enhance the performance of learning algorithm generalization ability from unlabelled data. Further, we integrate distillation technique to improve the performance on mobile platform. The experimental results demonstrate that the proposed segmentation model yields superior performance over competition. Since the processing of large medical images and distillation technology is enforced, a supercomputing AI training server is a preference for its application.

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Notes

  1. Eq. (15).

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Acknowledgement

The authors are grateful to King Saud University, Riyadh, Saudi Arabia for funding this work through Researchers Supporting Project Number RSP-2020/18.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

    Kuo-Kun Tseng & Ran Zhang

  2. Shandong University of Science and Technology, Shandong, China

    Chien-Ming Chen

  3. Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Mohammad Mehedi Hassan

Authors
  1. Kuo-Kun Tseng
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  2. Ran Zhang
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  3. Chien-Ming Chen
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  4. Mohammad Mehedi Hassan
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Correspondence to Mohammad Mehedi Hassan.

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Tseng, KK., Zhang, R., Chen, CM. et al. DNetUnet: a semi-supervised CNN of medical image segmentation for super-computing AI service. J Supercomput 77, 3594–3615 (2021). https://doi.org/10.1007/s11227-020-03407-7

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