AIMC Topic: Magnetic Resonance Imaging
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Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach.
Sensors (Basel, Switzerland)
Feb 28, 2025
BACKGROUND: Infant brain tissue segmentation from MRI data is a critical task in medical imaging, particularly challenging due to the evolving nature of tissue contrasts in the early months of life. The difficulty increases as gray matter (GM) and wh...
Preoperative diagnosis of meningioma sinus invasion based on MRI radiomics and deep learning: a multicenter study.
Cancer imaging : the official publication of the International Cancer Imaging Society
Feb 28, 2025
OBJECTIVE: Exploring the construction of a fusion model that combines radiomics and deep learning (DL) features is of great significance for the precise preoperative diagnosis of meningioma sinus invasion.
AutoDPS: An unsupervised diffusion model based method for multiple degradation removal in MRI.
Computer methods and programs in biomedicine
Feb 27, 2025
BACKGROUND AND OBJECTIVE: Diffusion models have demonstrated their ability in image generation and solving inverse problems like restoration. Unlike most existing deep-learning based image restoration techniques which rely on unpaired or paired data ...
T1-weighted MRI-based brain tumor classification using hybrid deep learning models.
Scientific reports
Feb 27, 2025
Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions....
Neurofind: using deep learning to make individualised inferences in brain-based disorders.
Translational psychiatry
Feb 27, 2025
Within precision psychiatry, there is a growing interest in normative models given their ability to parse heterogeneity. While they are intuitive and informative, the technical expertise and resources required to develop normative models may not be a...
Multi-parametric MRI Habitat Radiomics Based on Interpretable Machine Learning for Preoperative Assessment of Microsatellite Instability in Rectal Cancer.
Academic radiology
Feb 26, 2025
RATIONALE AND OBJECTIVES: This study constructed an interpretable machine learning model based on multi-parameter MRI sub-region habitat radiomics and clinicopathological features, aiming to preoperatively evaluate the microsatellite instability (MSI...
Post-Mortem imaging biobanks: Building data for reproducibility, standardization, and AI integration.
European journal of radiology
Feb 26, 2025
In recent years, post-mortem imaging has advanced with techniques such as Post-Mortem Computed Tomography (PMCT) and Post-Mortem Magnetic Resonance imaging (PMMR). PMCT is particularly useful for assessing skeletal injuries, vascular lesions, and est...
Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.
Chinese medical journal
Feb 26, 2025
Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emi...
Deep Learning for Lumbar Disc Herniation Diagnosis and Treatment Decision-Making Using Magnetic Resonance Imagings: A Retrospective Study.
World neurosurgery
Feb 26, 2025
BACKGROUND: Lumbar disc herniation (LDH) is a common cause of back and leg pain. Diagnosis relies on clinical history, physical exam, and imaging, with magnetic resonance imaging (MRI) being an important reference standard. While artificial intellige...
CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.
BMC geriatrics
Feb 26, 2025
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impacts health care worldwide, particularly among the elderly population. The accurate classification of AD stages is essential for slowing disease progression an...
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