AIMC Topic: Magnetic Resonance Imaging
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Shuffle-ResNet: Deep learning for predicting LGG IDH1 mutation from multicenter anatomical MRI sequences.
Biomedical physics & engineering express
Nov 11, 2022
The world health organization recommended to incorporate gene information such as isocitrate dehydrogenase 1 (IDH1) mutation status to improve prognosis, diagnosis, and treatment of the central nervous system tumors. We proposed our Shuffle Residual ...
Brain tumor classification based on neural architecture search.
Scientific reports
Nov 10, 2022
Brain tumor is a life-threatening disease and causes about 0.25 million deaths worldwide in 2020. Magnetic Resonance Imaging (MRI) is frequently used for diagnosing brain tumors. In medically underdeveloped regions, physicians who can accurately diag...
Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy.
IEEE journal of biomedical and health informatics
Nov 10, 2022
The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortic...
Self-Supervised Multi-Modal Hybrid Fusion Network for Brain Tumor Segmentation.
IEEE journal of biomedical and health informatics
Nov 10, 2022
Accurate medical image segmentation of brain tumors is necessary for the diagnosing, monitoring, and treating disease. In recent years, with the gradual emergence of multi-sequence magnetic resonance imaging (MRI), multi-modal MRI diagnosis has playe...
Novel artificial intelligent transformer U-NET for better identification and management of prostate cancer.
Molecular and cellular biochemistry
Nov 9, 2022
Advancements in artificial intelligence (AI) strengthens life-altering technology that can not only reduce human workload but also enhance speed and efficiency in medicine. Medical image segmentation, for example, MRI analysis, is an arduous task for...
Multi-label classification of Alzheimer's disease stages from resting-state fMRI-based correlation connectivity data and deep learning.
Computers in biology and medicine
Nov 9, 2022
Alzheimer's disease is a neurodegenerative condition that gradually impairs cognitive abilities. Recently, various neuroimaging modalities and machine learning methods have surfaced to diagnose Alzheimer's disease. Resting-state fMRI is a neuroimagin...
Scalable graph neural network for NMR chemical shift prediction.
Physical chemistry chemical physics : PCCP
Nov 9, 2022
Graph neural networks (GNNs) have been proven effective in the fast and accurate prediction of nuclear magnetic resonance (NMR) chemical shifts of a molecule. Existing methods, despite their effectiveness, suffer from high space complexity and are th...
Model-based Deep Learning Reconstruction Using a Folded Image Training Strategy for Abdominal 3D T1-weighted Imaging.
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Nov 8, 2022
PURPOSE: To evaluate the feasibility of folded image training strategy (FITS) and the quality of images reconstructed using the improved model-based deep learning (iMoDL) network trained with FITS (FITS-iMoDL) for abdominal MR imaging.
Ultralow-Field-Strength MRI and Artificial Intelligence: How Low Can We Go and How High Can We Reach?
Radiology
Nov 8, 2022
Automated volume measurement of abdominal adipose tissue from entire abdominal cavity in Dixon MR images using deep learning.
Radiological physics and technology
Nov 8, 2022
The purpose of this study was to realize an automated volume measurement of abdominal adipose tissue from the entire abdominal cavity in Dixon magnetic resonance (MR) images using deep learning. Our algorithm involves a combination of extraction of t...
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