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[HTML][HTML] A sco** review of interpretability and explainability concerning artificial intelligence methods in medical imaging
Abstract Purpose To review eXplainable Artificial Intelligence/(XAI) methods available for
medical imaging/(MI). Method A sco** review was conducted following the Joanna Briggs …
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
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 …
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
Purpose Numerous techniques based on deep learning have been utilized in sparse view
computed tomography (CT) imaging. Nevertheless, the majority of techniques are …
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
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 …
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 …
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 …
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 …
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 …
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
Chest CT scans play an important role in diagnosing abnormalities associated with the
lungs, such as tuberculosis, sarcoidosis, pneumonia, and, more recently, COVID-19 …
lungs, such as tuberculosis, sarcoidosis, pneumonia, and, more recently, COVID-19 …
PILN: A posterior information learning network for blind reconstruction of lung CT images
Background and objective: Computer tomography (CT) imaging technology has played
significant roles in the diagnosis and treatment of various lung diseases, but the …
significant roles in the diagnosis and treatment of various lung diseases, but the …