AIMC Topic: Magnetic Resonance Imaging
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Convolutional neural network classification of ultrasound parametric images based on echo-envelope statistics for the quantitative diagnosis of liver steatosis.
Journal of medical ultrasonics (2001)
Nov 23, 2024
PURPOSE: Early detection and quantitative evaluation of liver steatosis are crucial. Therefore, this study investigated a method for classifying ultrasound images to fatty liver grades based on echo-envelope statistics (ES) and convolutional neural n...
Accelerated Spine MRI with Deep Learning Based Image Reconstruction: A Prospective Comparison with Standard MRI.
Academic radiology
Nov 22, 2024
RATIONALE AND OBJECTIVES: To evaluate the performance of deep learning (DL) reconstructed MRI in terms of image acquisition time, overall image quality and diagnostic interchangeability compared to standard-of-care (SOC) MRI.
Multi-task magnetic resonance imaging reconstruction using meta-learning.
Magnetic resonance imaging
Nov 22, 2024
Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the dissimilarity ...
Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis.
Computers in biology and medicine
Nov 22, 2024
Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information about the brain that can aid in Alzheimer's disease (AD) diagnosis. However, most existing deep learni...
Self-supervised learning on dual-sequence magnetic resonance imaging for automatic segmentation of nasopharyngeal carcinoma.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Nov 22, 2024
Automating the segmentation of nasopharyngeal carcinoma (NPC) is crucial for therapeutic procedures but presents challenges given the hurdles in amassing extensively annotated datasets. Although previous studies have applied self-supervised learning ...
Automatic pituitary adenoma segmentation and identification of cavernous sinus invasion via multitask learning.
Clinical radiology
Nov 21, 2024
AIM: This study aimed to develop a multitask deep learning model for pituitary macroadenoma (PMA) segmentation and identification of cavernous sinus (CS) invasion.
A novel approach for brain connectivity using recurrent neural networks and integrated gradients.
Computers in biology and medicine
Nov 21, 2024
Brain connectivity is an important tool for understanding the cognitive and perceptive neural mechanisms in the neuroimaging field. Many methods for estimating effective connectivity have relied on the linear regressive model. However, the linear reg...
A Deep Dynamic Causal Learning Model to Study Changes in Dynamic Effective Connectivity During Brain Development.
IEEE transactions on bio-medical engineering
Nov 21, 2024
OBJECTIVE: Brain dynamic effective connectivity (dEC), characterizes the information transmission patterns between brain regions that change over time, which provides insight into the biological mechanism underlying brain development. However, most e...
Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas.
Radiation oncology (London, England)
Nov 21, 2024
INTRODUCTION: The delineation of organs-at-risk and lymph node areas is a crucial step in radiotherapy, but it is time-consuming and associated with substantial user-dependent variability in contouring. Artificial intelligence (AI) appears to be the ...
The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning.
Scientific reports
Nov 21, 2024
This work is to investigate the diagnostic value of a deep learning-based magnetic resonance imaging (MRI) image segmentation (IS) technique for shoulder joint injuries (SJIs) in swimmers. A novel multi-scale feature fusion network (MSFFN) is develop...
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