Transforming unmanned pineapple picking with spatio-temporal convolutional neural networks
F Meng, J Li, Y Zhang, S Qi, Y Tang - Computers and Electronics in …, 2023 - Elsevier
Automated pineapple harvesting has emerged as a prominent prospective development
within the agricultural domain. Nevertheless, the intricate growth conditions that pineapples …
within the agricultural domain. Nevertheless, the intricate growth conditions that pineapples …
Is attention all you need in medical image analysis? A review.
Medical imaging is a key component in clinical diagnosis, treatment planning and clinical
trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance …
trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance …
A systematic review and identification of the challenges of deep learning techniques for undersampled magnetic resonance image reconstruction
Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …
SwinGAN: A dual-domain Swin Transformer-based generative adversarial network for MRI reconstruction
X Zhao, T Yang, B Li, X Zhang - Computers in Biology and Medicine, 2023 - Elsevier
Magnetic resonance imaging (MRI) is one of the most important modalities for clinical
diagnosis. However, the main disadvantages of MRI are the long scanning time and the …
diagnosis. However, the main disadvantages of MRI are the long scanning time and the …
A physics-guided deep learning approach for functional assessment of cardiovascular disease in IoT-based smart health
The rapid development of the Internet of Things (IoT) widely supports the smart healthcare
system. IoT-based smart health has significant importance for the diagnosis of …
system. IoT-based smart health has significant importance for the diagnosis of …
Humus-net: Hybrid unrolled multi-scale network architecture for accelerated mri reconstruction
In accelerated MRI reconstruction, the anatomy of a patient is recovered from a set of
undersampled and noisy measurements. Deep learning approaches have been proven to …
undersampled and noisy measurements. Deep learning approaches have been proven to …
ReconFormer: Accelerated MRI reconstruction using recurrent transformer
The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging
ill-posed inverse problem due to the excessive under-sampling operation in-space. In this …
ill-posed inverse problem due to the excessive under-sampling operation in-space. In this …
Iterative residual optimization network for limited-angle tomographic reconstruction
Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems,
leading to edge divergence with degraded image quality. Recently, deep learning has been …
leading to edge divergence with degraded image quality. Recently, deep learning has been …
Hierarchical perception adversarial learning framework for compressed sensing MRI
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI)
because it leads to patient discomfort and motion artifacts. Although several MRI techniques …
because it leads to patient discomfort and motion artifacts. Although several MRI techniques …
Swin deformable attention u-net transformer (sdaut) for explainable fast mri
Fast MRI aims to reconstruct a high fidelity image from partially observed measurements.
Exuberant development in fast MRI using deep learning has been witnessed recently …
Exuberant development in fast MRI using deep learning has been witnessed recently …