A review of deep learning ct reconstruction from incomplete projection data

T Wang, W Xia, J Lu, Y Zhang - IEEE Transactions on Radiation …, 2023 - ieeexplore.ieee.org
Computed tomography (CT) is a widely used imaging technique in both medical and
industrial applications. However, accurate CT reconstruction requires complete projection …

Hypernetwork-based physics-driven personalized federated learning for CT imaging

Z Yang, W Xia, Z Lu, Y Chen, X Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In clinical practice, computed tomography (CT) is an important noninvasive inspection
technology to provide patients' anatomical information. However, its potential radiation risk is …

A review of AutoML optimization techniques for medical image applications

MJ Ali, M Essaid, L Moalic, L Idoumghar - Computerized Medical Imaging …, 2024 - Elsevier
Automatic analysis of medical images using machine learning techniques has gained
significant importance over the years. A large number of approaches have been proposed …

LEARN++: recurrent dual-domain reconstruction network for compressed sensing CT

Y Zhang, H Chen, W Xia, Y Chen, B Liu… - … on Radiation and …, 2022 - ieeexplore.ieee.org
Compressed sensing (CS) computed tomography (CT) has been proven to be important for
several clinical applications, such as sparse-view CT, digital tomosynthesis, and interior …

RegFormer: A Local–Nonlocal Regularization-Based Model for Sparse-View CT Reconstruction

W Xia, Z Yang, Z Lu, Z Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Sparse-view computed tomography (CT) is one of the primal means to reduce radiation risk.
However, the reconstruction of sparse-view CT with the classic analytical method is usually …

SemiMAR: Semi-supervised learning for CT metal artifact reduction

T Wang, H Yu, Z Wang, H Chen, Y Liu… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Metal artifacts lead to CT imaging quality degradation. With the success of deep learning
(DL) in medical imaging, a number of DL-based supervised methods have been developed …

COVID-19 classification on chest X-ray images using deep learning methods

M Constantinou, T Exarchos, AG Vrahatis… - International Journal of …, 2023 - mdpi.com
Since December 2019, the coronavirus disease has significantly affected millions of people.
Given the effect this disease has on the pulmonary systems of humans, there is a need for …

DDoCT: Morphology preserved dual-domain joint optimization for fast sparse-view low-dose CT imaging

L Li, Z Zhang, Y Li, Y Wang, W Zhao - Medical Image Analysis, 2025 - Elsevier
Computed tomography (CT) is continuously becoming a valuable diagnostic technique in
clinical practice. However, the radiation dose exposure in the CT scanning process is a …

Hformer: highly efficient vision transformer for low-dose CT denoising

SY Zhang, ZX Wang, HB Yang, YL Chen, Y Li… - Nuclear Science and …, 2023 - Springer
In this paper, we propose Hformer, a novel supervised learning model for low-dose
computer tomography (LDCT) denoising. Hformer combines the strengths of convolutional …

Energizing Federated Learning via Filter-Aware Attention

Z Yang, Z Shao, H Huangfu, H Yu, ABJ Teoh… - arXiv preprint arXiv …, 2023 - arxiv.org
Federated learning (FL) is a promising distributed paradigm, eliminating the need for data
sharing but facing challenges from data heterogeneity. Personalized parameter generation …