Reconstructing and analyzing the invariances of low‐dose CT image denoising networks
The proposed method is capable of reconstructing and analyzing invariances of deep learning-based low-dose CT image denoising networks. This is an important step toward interpreting deep learning-based methods for medical imaging, which is essential for their clinical implementation.
Authors
- Fabian Jäger
- Joscha Maier (ORCID: https://orcid.org/0000-0001-5042-0511)
- Marc Kachelrieß (ORCID: https://orcid.org/0000-0001-9351-4761)
- Elias Eulig (ORCID: https://orcid.org/0000-0001-6269-6320)
- Björn Ommer (ORCID: https://orcid.org/0000-0003-0766-120X)
Institutions
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- LMU Klinikum (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-09-30
- DOI
- https://doi.org/10.1002/mp.17413
- Citations
- 3
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.21