MM-Net-PyTorch (IEEE, 2022)

code for MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography, IEEE. We used the clinical dataset of the 2016 NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge https://ieeexplore.ieee.org/document/9963593

I have created a GitHub repository to share the code for the paper 'MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography.' I am currently working on it. If you are interested in sharing the code, please feel free to contact me at sunyounge_@ewhain.net. I will update it soon.

Overall architecture

Two-step training network

First Training Step :

Multiscale Attention U-Net

The code for attention U-Net can be found at https://github.com/LeeJunHyun/Image_Segmentation. You can find more detailed networks available there.

Second Training Step :

Multipatch and Multi-mask

Citation

You may cite this project as:

@ARTICLE{9963593,
  author={Jeon, Sun-Young and Kim, Wonjin and Choi, Jang-Hwan},
  journal={IEEE Transactions on Radiation and Plasma Medical Sciences}, 
  title={MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography}, 
  year={2023},
  volume={7},
  number={3},
  pages={296-306},
  doi={10.1109/TRPMS.2022.3224553}}