AIMC Topic: Magnetic Resonance Imaging

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LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI.

IEEE journal of biomedical and health informatics
Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accurately diagnose and predict it at an early stage. However, the existing methods have shortcomings, such as inability to learn local and global informa...

Self-Supervised Multi-Scale Multi-Modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.

IEEE journal of biomedical and health informatics
The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of pat...

Robust Sensory Information Reconstruction and Classification With Augmented Spikes.

IEEE transactions on neural networks and learning systems
Sensory information recognition is primarily processed through the ventral and dorsal visual pathways in the primate brain visual system, which exhibits layered feature representations bearing a strong resemblance to convolutional neural networks (CN...

Deep Geometric Learning With Monotonicity Constraints for Alzheimer's Disease Progression.

IEEE transactions on neural networks and learning systems
Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progression over time is vital for clinical diagnosis and treatment. For this, numerous studies have imple...

Brain tumor segmentation and detection in MRI using convolutional neural networks and VGG16.

Cancer biomarkers : section A of Disease markers
BackgroundIn this research, we explore the application of Convolutional Neural Networks (CNNs) for the development of an automated cancer detection system, particularly for MRI images. By leveraging deep learning and image processing techniques, we a...

An interpretable radiomics-based machine learning model for predicting reverse left ventricular remodeling in STEMI patients using late gadolinium enhancement of myocardial scar.

European radiology
OBJECTIVES: To evaluate the added value of the late gadolinium enhancement (LGE)-scar radiomics features in predicting reverse left ventricular remodeling (r-LVR) in ST-segment elevation myocardial infarction (STEMI) patients using machine learning (...

Temporal interactions between neural proxies for memory recall, negative affect, and emotion regulation in major depression.

Molecular psychiatry
Dysfunction in emotion regulation (ER) and autobiographical memory are components of major depressive disorder (MDD). However, little is known about how they mechanistically interact with mood disturbances in real time. Using machine learning-based n...

Effects of Renal Function on the Multimodal Brain Networks Affecting Mild Cognitive Impairment Converters in End-Stage Renal Disease.

Academic radiology
RATIONALE AND OBJECTIVES: Cognitive decline is common in End-Stage Renal Disease (ESRD) patients, yet its neural mechanisms are poorly understood. This study investigates structural and functional brain network reconfiguration in ESRD patients transi...

Radiomics across modalities: a comprehensive review of neurodegenerative diseases.

Clinical radiology
Radiomics allows extraction from medical images of quantitative features that are able to reveal tissue patterns that are generally invisible to human observers. Despite the challenges in visually interpreting radiomic features and the computational ...

Early prediction of neoadjuvant therapy response in breast cancer using MRI-based neural networks: data from the ACRIN 6698 trial and a prospective Chinese cohort.

Breast cancer research : BCR
BACKGROUND: Early prediction of treatment response to neoadjuvant therapy (NAT) in breast cancer patients can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a MRI-based enhanced self-attention network (MESN) for ...