AIMC Topic: Brain
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Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.
Neuroinformatics
Nov 6, 2024
The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compa...
Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning versus deep learning.
Biomedical physics & engineering express
Nov 5, 2024
. Virtual reality (VR) simulates real-life events and scenarios and is widely utilized in education, entertainment, and medicine. VR can be presented in two dimensions (2D) or three dimensions (3D), with 3D VR offering a more realistic and immersive ...
Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.
Neurology
Nov 4, 2024
BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific eff...
Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.
Sensors (Basel, Switzerland)
Nov 4, 2024
Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and med...
Design of EEG based thought identification system using EMD & deep neural network.
Scientific reports
Nov 4, 2024
Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based mes...
BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.
IEEE transactions on medical imaging
Nov 4, 2024
Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical ...
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction.
IEEE transactions on medical imaging
Nov 4, 2024
Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifa...
A protocol for trustworthy EEG decoding with neural networks.
Neural networks : the official journal of the International Neural Network Society
Nov 2, 2024
Deep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art performance on a variety of decoding tasks. Despite their high performance, existing solutions do not fully address the challenge posed by the introduction of m...
Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.
Magnetic resonance in medicine
Oct 31, 2024
PURPOSE: External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always poss...
Effective deep-learning brain MRI super resolution using simulated training data.
Computers in biology and medicine
Oct 31, 2024
BACKGROUND: In the field of medical imaging, high-resolution (HR) magnetic resonance imaging (MRI) is essential for accurate disease diagnosis and analysis. However, HR imaging is prone to artifacts and is not universally available. Consequently, low...
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