AIMC Topic: Brain
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Classification of Irritable Bowel Syndrome Using Brain Functional Connectivity Strength and Machine Learning.
Neurogastroenterology and motility
Jan 3, 2025
BACKGROUND: Irritable Bowel Syndrome (IBS) is a prevalent condition characterized by dysregulated brain-gut interactions. Despite its widespread impact, the brain mechanism of IBS remains incompletely understood, and there is a lack of objective diag...
DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN-Transformer Network.
Sensors (Basel, Switzerland)
Jan 3, 2025
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by variou...
Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.
BMC neurology
Jan 3, 2025
BACKGROUND AND PURPOSE: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a...
Dynamics of specialization in neural modules under resource constraints.
Nature communications
Jan 2, 2025
The brain is structurally and functionally modular, although recent evidence has raised questions about the extent of both types of modularity. Using a simple, toy artificial neural network setup that allows for precise control, we find that structur...
SISMIK for Brain MRI: Deep-Learning-Based Motion Estimation and Model-Based Motion Correction in k-Space.
IEEE transactions on medical imaging
Jan 2, 2025
MRI, a widespread non-invasive medical imaging modality, is highly sensitive to patient motion. Despite many attempts over the years, motion correction remains a difficult problem and there is no general method applicable to all situations. We propos...
Bridging MRI Cross-Modality Synthesis and Multi-Contrast Super-Resolution by Fine-Grained Difference Learning.
IEEE transactions on medical imaging
Jan 2, 2025
In multi-modal magnetic resonance imaging (MRI), the tasks of imputing or reconstructing the target modality share a common obstacle: the accurate modeling of fine-grained inter-modal differences, which has been sparingly addressed in current literat...
Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging.
IEEE transactions on medical imaging
Jan 2, 2025
Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In Machine Learning, this ambiguity is commonl...
AutoSamp: Autoencoding k-Space Sampling via Variational Information Maximization for 3D MRI.
IEEE transactions on medical imaging
Jan 2, 2025
Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we...
Boosting Your Context by Dual Similarity Checkup for In-Context Learning Medical Image Segmentation.
IEEE transactions on medical imaging
Jan 2, 2025
The recent advent of in-context learning (ICL) capabilities in large pre-trained models has yielded significant advancements in the generalization of segmentation models. By supplying domain-specific image-mask pairs, the ICL model can be effectively...
Consistency-Guided Differential Decoding for Enhancing Semi-Supervised Medical Image Segmentation.
IEEE transactions on medical imaging
Jan 2, 2025
Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data, especially on volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly confident pseudo-labels or de...
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