FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count...

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Vydáno v:	Medical image analysis Ročník 90; s. 102993
Hlavní autoři:	Zhou, Bo, Xie, Huidong, Liu, Qiong, Chen, Xiongchao, Guo, Xueqi, Feng, Zhicheng, Hou, Jun, Zhou, S. Kevin, Li, Biao, Rominger, Axel, Shi, Kuangyu, Duncan, James S., Liu, Chi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 01.12.2023
Témata:	Algorithms Deep reconstruction Denoising Humans Image Processing, Computer-Assisted Low-count PET Personalized federated learning Positron-Emission Tomography Signal-To-Noise Ratio Personalized federated learning 41A10 65D05 Deep reconstruction 65D17 Low-count PET 41A05 Denoising
ISSN:	1361-8415, 1361-8423, 1361-8423
On-line přístup:	Získat plný text
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