AIMC Topic: Magnetic Resonance Imaging
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Application of magnetic resonance imaging and artificial intelligence algorithms in cancer screening.
SLAS technology
Nov 17, 2024
In this society with a high incidence of cancer, cancer screening has become an important method to reduce the incidence and mortality of cancer. Traditional cancer screening methods such as CT have certain limitations and are difficult to adapt to l...
Generalizable Magnetic Resonance Imaging-based Nasopharyngeal Carcinoma Delineation: Bridging Gaps Across Multiple Centers and Raters With Active Learning.
International journal of radiation oncology, biology, physics
Nov 16, 2024
PURPOSE: To develop a deep learning method exploiting active learning and source-free domain adaptation for gross tumor volume delineation in nasopharyngeal carcinoma (NPC), addressing the variability and inaccuracy when deploying segmentation models...
Semantic-spatial feature-fused cortical surface parcellation: a scale-unified spatial learning network with boundary contrastive loss.
Medical & biological engineering & computing
Nov 16, 2024
The cortical surface parcellation provides prior guidance for studying mental disorders and human cognition. Graph neural networks (GNNs) have gained popularity in this task to preserve its spatial structure. However, previous GNNs struggled to effec...
Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.
Japanese journal of radiology
Nov 16, 2024
In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce t...
Automatic TNM staging of colorectal cancer radiology reports using pre-trained language models.
Computer methods and programs in biomedicine
Nov 16, 2024
BACKGROUND AND OBJECTIVE: Colorectal cancer is one of the major causes of cancer death worldwide. Essential for prognosis and treatment planning, TNM staging offers critical insights into the advancement of colorectal cancer. However, manual TNM stag...
Recognition of autism in subcortical brain volumetric images using autoencoding-based region selection method and Siamese Convolutional Neural Network.
International journal of medical informatics
Nov 16, 2024
BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social interactions and behavior. Accurate and early diagnosis of ASD is still challenging even with the improvements in neuroimaging technology and machine lea...
Validation of SynthSeg segmentation performance on CT using paired MRI from radiotherapy patients.
NeuroImage
Nov 16, 2024
INTRODUCTION: Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this ex...
An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.
Medical & biological engineering & computing
Nov 15, 2024
Alzheimer's disease (AD) refers to a neurological disorder that causes damage to brain cells and results in decreasing cognitive abilities and memory. In brain scans, this degeneration can be seen in different ways. The disease can be classified into...
Structural-based uncertainty in deep learning across anatomical scales: Analysis in white matter lesion segmentation.
Computers in biology and medicine
Nov 15, 2024
This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segmentation from magnetic resonance imaging (MRI) scans of multiple sclerosi...
Comparison of different acceleration factors of artificial intelligence-compressed sensing for brachial plexus MRI imaging: scanning time and image quality.
BMC medical imaging
Nov 14, 2024
BACKGROUND: 3D brachial plexus MRI scanning is prone to examination failure due to the lengthy scan times, which can lead to patient discomfort and motion artifacts. Our purpose is to investigate the efficacy of artificial intelligence-assisted compr...
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