Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer's Disease: A Comprehensive Review
Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions of people
worldwide, making early detection essential for effective intervention. This review paper …
worldwide, making early detection essential for effective intervention. This review paper …
A systematic review of vision transformers and convolutional neural networks for Alzheimer's disease classification using 3D MRI images
MA Bravo-Ortiz, SA Holguin-Garcia… - Neural Computing and …, 2024 - Springer
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that mainly affects
memory and other cognitive functions, such as thinking, reasoning, and the ability to carry …
memory and other cognitive functions, such as thinking, reasoning, and the ability to carry …
S2SNet: Two-stream geometry-aware sequence to sequence network for robot motion skills learning and generalization
X Xu, K Qian, B Zhou, F Fang - Advanced Engineering Informatics, 2024 - Elsevier
Pick-and-place manipulation is a pivotal component in many robotic tasks. In unstructured,
dynamic and uncertain environments, controllers are required to learn and generalize pick …
dynamic and uncertain environments, controllers are required to learn and generalize pick …
Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review
Abstract Background and Aims Alzheimer's disease (AD) is a degenerative neurological
condition that worsens over time and leads to deterioration in cognitive abilities, reduced …
condition that worsens over time and leads to deterioration in cognitive abilities, reduced …
WaveSeg‐UNet model for overlapped nuclei segmentation from multi‐organ histopathology images
Nuclei segmentation is a challenging task in histopathology images. It is challenging due to
the small size of objects, low contrast, touching boundaries, and complex structure of nuclei …
the small size of objects, low contrast, touching boundaries, and complex structure of nuclei …
USCT-UNet: Rethinking the Semantic Gap in U-Net Network from U-shaped Skip Connections with Multichannel Fusion Transformer
X Xie, M Yang - IEEE Transactions on Neural Systems and …, 2024 - ieeexplore.ieee.org
Medical image segmentation is a crucial component of computer-aided clinical diagnosis,
with state-of-the-art models often being variants of U-Net. Despite their success, these …
with state-of-the-art models often being variants of U-Net. Despite their success, these …
Helicopter cockpit speech recognition method based on transfer learning and context biasing
G Wang, J Wang, S Wang… - Engineering …, 2024 - pubishingsupport.iopscience.iop.org
Currently, Chinese speech recognition technology is generally designed for common
domains, primarily focusing on accurate recognition of standard Mandarin Chinese in low …
domains, primarily focusing on accurate recognition of standard Mandarin Chinese in low …
[HTML][HTML] Stroke Prognostication in Patients Treated with Thrombolysis Using Random Forest
RE Yunus, S Harris… - The Open …, 2024 - openneuroimagingjournal.com
Background Early identification and accurate prognostication of acute ischemic stroke are
crucial due to the narrow time frame for treatment and potential complications associated …
crucial due to the narrow time frame for treatment and potential complications associated …
Intelligent Segmentor: Self Supervised Deep Learning based Multi Organ and Tumor Segmentation with Pseudo Lables Generation from CT Images
Multi Organ and tumor segmentation is the challenging task in medical imaging and surgical
planning scenarios due to its diverse applications includes lesions and organs …
planning scenarios due to its diverse applications includes lesions and organs …