[HTML][HTML] A sco** review of interpretability and explainability concerning artificial intelligence methods in medical imaging

M Champendal, H Müller, JO Prior… - European journal of …, 2023 - Elsevier
Abstract Purpose To review eXplainable Artificial Intelligence/(XAI) methods available for
medical imaging/(MI). Method A sco** review was conducted following the Joanna Briggs …

Deep residual constrained reconstruction via learned convolutional sparse coding for low-dose CT imaging

J Liu, T Zhang, Y Kang, Y Wang, Y Zhang, D Hu… - … Signal Processing and …, 2023 - Elsevier
Low-dose computed tomography (LDCT) holds great potential to reduce radiation dose
damage. However, LDCT degrades the signal-to-noise ratio (SNR) of projection and …

Deep convolutional dictionary learning network for sparse view CT reconstruction with a group sparse prior

Y Kang, J Liu, F Wu, K Wang, J Qiang, D Hu… - Computer Methods and …, 2024 - Elsevier
Purpose Numerous techniques based on deep learning have been utilized in sparse view
computed tomography (CT) imaging. Nevertheless, the majority of techniques are …

[HTML][HTML] An efficient sinogram domain fully convolutional interpolation network for sparse-view computed tomography reconstruction

F Guo, B Yang, H Feng, W Zheng, L Yin, Z Yin, C Liu - Applied Sciences, 2023 - mdpi.com
Recently, deep learning techniques have been used for low-dose CT (LDCT) reconstruction
to reduce the radiation risk for patients. Despite the improvement in performance, the …

SureUnet: sparse autorepresentation encoder U-Net for noise artifact suppression in low-dose CT

J Liu, T Zhang, Y Kang, J Qiang, D Hu… - Neural Computing and …, 2023 - Springer
Low-dose computed tomography (LDCT) is desirable due to ionizing radiation, but the
resulting images suffer from serious streak artifacts and spot noise. Recently, deep learning …

Low dose computed tomography reconstruction with momentum-based frequency adjustment network

Q Sun, N He, P Yang, X Zhao - Computer Methods and Programs in …, 2025 - Elsevier
Abstract Background and Objective: Recent investigations into Low-Dose Computed
Tomography (LDCT) reconstruction methods have brought Model-Based Data-Driven …

Impact of intelligent convolutional neural network-based algorithms on head computed tomography evaluation and comprehensive rehabilitation acupuncture therapy …

J Chen, J Zhang, J **ang, J Yu, F Qiu - Journal of Neuroscience Methods, 2024 - Elsevier
This work was to evaluate the impacts of comprehensive rehabilitation acupuncture therapy
on the recovery of neurological function in cerebral infarction (CI) patients and to utilize …

Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging

Z **a, J Liu, Y Kang, Y Wang, D Hu… - Quantitative Imaging in …, 2023 - pmc.ncbi.nlm.nih.gov
Background Computed tomography (CT) imaging technology has become an indispensable
auxiliary method in medical diagnosis and treatment. In mitigating the radiation damage …

Impact of Loss Function on the Performance of COVID-19 CT Image Denoising

A Chaturvedi, R Prabhu, M Yadav… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Chest CT scans play an important role in diagnosing abnormalities associated with the
lungs, such as tuberculosis, sarcoidosis, pneumonia, and, more recently, COVID-19 …

PILN: A posterior information learning network for blind reconstruction of lung CT images

J Chi, Z Sun, X Han, X Yu, H Wang, C Wu - Computer Methods and …, 2023 - Elsevier
Background and objective: Computer tomography (CT) imaging technology has played
significant roles in the diagnosis and treatment of various lung diseases, but the …