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
Final Paper
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.
Important Date
  • Conference Date

    Jul 11

    2023

    to

    Aug 18

    2023

  • Nov 10 2021

    Draft paper submission deadline

  • Dec 10 2021

    Registration deadline

  • Dec 11 2021

    Contribution Submission Deadline

Sponsored By
IEEE IAS
Organized By
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