Deep residual learning with Anscombe transformation for low-dose digital tomosynthesis
Crossref DOI link: https://doi.org/10.1007/s40042-024-01117-4
Published Online: 2024-06-17
Published Print: 2024-08
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Lee, Youngjin
Lee, Seungwan
Park, Chanrok
Text and Data Mining valid from 2024-06-17
Version of Record valid from 2024-06-17
Article History
Received: 6 March 2024
Revised: 7 May 2024
Accepted: 5 June 2024
First Online: 17 June 2024
Declarations
:
: The author declares that there is no conflict of interest.