243 / 2021-11-04 10:47:17
Self-Distillation for Low-Dose CT Image Denoising
deep learning,denoising,knowledge distillation,low-dose CT,neural network
终稿
Hang Mou / Sichuan University
Wenjun Xia / Sichuan University
Zi-Yuan Yang / Sichuan University
Jiliu Zhou / Sichuan University
Yi Zhang / Sichuan University
As a medical imaging technique, computed tomography (CT) has been widely used in clinical internal visualization, lesion detection and disease tracking. But excessive radiation will cause adverse effects on patients. Lowering the radiation dose can alleviate this problem, but it will lead to the degradation of CT images. In this paper, we propose a knowledge distillation-based denoising method for low-dose CT images. The teacher network trained with higher dose data can generate soft labels for the student network to avoid information loss. The experiments prove that the model trained with our proposed method outperforms the original model in terms of detail preservation.
重要日期
  • 会议日期

    07月11日

    2023

    08月18日

    2023

  • 11月10日 2021

    初稿截稿日期

  • 12月10日 2021

    注册截止日期

  • 12月11日 2021

    报告提交截止日期

主办单位
IEEE IAS
承办单位
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST
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