Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges

Z Chen, K Pawar, M Ekanayake, C Pain, S Zhong… - Journal of Digital …, 2023 - Springer
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …

[HTML][HTML] Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods

SA Mali, A Ibrahim, HC Woodruff… - Journal of personalized …, 2021 - mdpi.com
Radiomics converts medical images into mineable data via a high-throughput extraction of
quantitative features used for clinical decision support. However, these radiomic features are …

Convolutional neural network in medical image analysis: a review

SS Kshatri, D Singh - Archives of Computational Methods in Engineering, 2023 - Springer
Medical image analysis helps in resolving clinical issues by examining clinically generated
images. In today's world of deep learning (DL) along with advances in computer vision, the …

Multimodal transformer for accelerated MR imaging

CM Feng, Y Yan, G Chen, Y Xu, Y Hu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution
for fast MR imaging, providing superior performance in restoring the target modality from its …

Deep learning in generating radiology reports: A survey

MMA Monshi, J Poon, V Chung - Artificial Intelligence in Medicine, 2020 - Elsevier
Substantial progress has been made towards implementing automated radiology reporting
models based on deep learning (DL). This is due to the introduction of large medical …