Low-dose CT denoising via CNN trained using images with activation map

Minah Han, Jongduk Baek

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Convolutional neural network (CNN) based denoisers have shown promising results in low-dose CT (LDCT) denoising. However, image blur is a problem that needs to be addressed because it deforms or eliminates small features, which interferes with the diagnosis. Pixel level loss, such as mean-squared-error (MSE) loss, used for CNN training is the cause of the image blur in the denoised image because the pixel level loss computes the average of all pixel value differences without attention on important features. To resolve the image blur, we propose to use an activation map for training CNN denoiser. The activation map indicates the area where the CNN classifier focuses for classifying the image. We train CNN classifier to classify lesion-present and lesion-Absent CT images (i.e., binary detection task), and then, obtain activation map of image using trained CNN classifier. It is observed that lesions and edges of images are activated in activation map, and therefore, when the activation map is multiplied by image, small features are emphasized. We train CNN denoiser in two steps. First, we train CNN denoiser using LDCT and normal-dose CT (NDCT) image pairs. In the second step, we fine-Tune network parameters of CNN denoiser using LDCT and NDCT image pairs multiplied by NDCT activation map. The twostep trained CNN denoiser effectively reduces noise while preserving small features.

Original languageEnglish
Title of host publicationMedical Imaging 2022
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsClaudia R. Mello-Thoms, Claudia R. Mello-Thoms, Sian Taylor-Phillips
ISBN (Electronic)9781510649453
Publication statusPublished - 2022
EventMedical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment - Virtual, Online
Duration: 2022 Mar 212022 Mar 27

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment
CityVirtual, Online

Bibliographical note

Funding Information:
This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (202011D06 and 202011A03) and the Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (2019R1A2C2084936 and 2020R1A4A1016619).

Publisher Copyright:
© 2022 SPIE. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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