AIMC Topic: Brain
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PASS: Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation.
IEEE transactions on medical imaging
Apr 3, 2025
Test-time adaptation (TTA) has emerged as a promising paradigm to handle the domain shifts at test time for medical images from different institutions without using extra training data. However, existing TTA solutions for segmentation tasks suffer fr...
POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation.
IEEE transactions on medical imaging
Apr 3, 2025
Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans for generating attenuation maps ( -map) for PET attenuation correction significantly elevates radia...
Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.
AJNR. American journal of neuroradiology
Apr 2, 2025
BACKGOUND AND PURPOSE: This study utilizes a physics-based approach to synthesize realistic MR artifacts and train a deep learning generative adversarial network (GAN) for use in artifact reduction on EPI, a crucial neuroimaging sequence with high ac...
Comprehensive Segmentation of Gray Matter Structures on T1-Weighted Brain MRI: A Comparative Study of Convolutional Neural Network, Convolutional Neural Network Hybrid-Transformer or -Mamba Architectures.
AJNR. American journal of neuroradiology
Apr 2, 2025
BACKGROUND AND PURPOSE: Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their data sets and/or the number of structures t...
Enhancing brain age estimation under uncertainty: A spectral-normalized neural gaussian process approach utilizing 2.5D slicing.
NeuroImage
Apr 1, 2025
Brain age gap, the difference between estimated brain age and chronological age via magnetic resonance imaging, has emerged as a pivotal biomarker in the detection of brain abnormalities. While deep learning is accurate in estimating brain age, the a...
A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 1, 2025
Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Fun...
Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.
Annals of the New York Academy of Sciences
Mar 30, 2025
Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment....
Multivariate Classification of Adolescent Major Depressive Disorder Using Whole-brain Functional Connectivity.
Academic radiology
Mar 29, 2025
RATIONALE AND OBJECTIVES: Adolescent major depressive disorder (MDD) is a serious mental health condition that has been linked to abnormal functional connectivity (FC) patterns within the brain. However, whether FC could be used as a potential biomar...
Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI.
Computers in biology and medicine
Mar 29, 2025
This paper introduces a novel convolutional neural network model with an attention mechanism to advance Alzheimer disease (AD) classification using Magnetic Resonance Imaging (MRI). The model architecture is meticulously crafted to enhance feature ex...
Deep structural brain imaging via computational three-photon microscopy.
Journal of biomedical optics
Mar 29, 2025
SIGNIFICANCE: High-resolution optical imaging at significant depths is challenging due to scattering, which impairs image quality in living matter with complex structures. We address the need for improved imaging techniques in deep tissues.
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