AIMC Topic: Neuroimaging

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Major depression disorder diagnosis and analysis based on structural magnetic resonance imaging and deep learning.

Journal of integrative neuroscience
Major depression disorder is one of the diseases with the highest rate of disability and morbidity and is associated with numerous structural and functional differences in neural systems. However, it is difficult to analyze digital medical imaging da...

Shedding light on pain for the clinic: a comprehensive review of using functional near-infrared spectroscopy to monitor its process in the brain.

Pain
Pain is a complex experience that involves sensation, emotion, and cognition. The subjectivity of the traditional pain measurement tools has expedited the interest in developing neuroimaging techniques to monitor pain objectively. Among noninvasive n...

Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Neuropsychiatric disorders such as schizophrenia are very heterogeneous in nature and typically diagnosed using self-reported symptoms. This makes it difficult to pose a confident prediction on the cases and does not provide insight into the underlyi...

Input Agnostic Deep Learning for Alzheimer's Disease Classification Using Multimodal MRI Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. The advances in machine learning and publicly available medical datasets initiated multiple studies in AD diagnosis. In this work, we utilize a mu...

Learning From Mouse CT-Scan Brain Images To Detect MRA-TOF Human Vasculatures.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The earlier studies on brain vasculature semantic segmentation used classical image analysis methods to extract the vascular tree from images. Nowadays, deep learning methods are widely exploited for various image analysis tasks. One of the strong re...

Data-Limited Deep Learning Methods for Mild Cognitive Impairment Classification in Alzheimer's Disease Patients.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Mild Cognitive Impairment (MCI) is the stage between the declining of normal brain function and the more serious decline of dementia. Alzheimer's disease (AD) is one of the leading forms of dementia. Although MCI does not always lead to AD, an early ...

Deep Learning on SDF for Classifying Brain Biomarkers.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Biomarkers are one of the primary medical signs to facilitate the early detection of Alzheimer's disease. The small beta-amyloid (Aβ) peptide is an important indicator for the disease. However, current methods to detect Aβ pathology are either invasi...

Clinical Artificial Intelligence Applications in Radiology: Neuro.

Radiologic clinics of North America
Radiologists have been at the forefront of the digitization process in medicine. Artificial intelligence (AI) is a promising area of innovation, particularly in medical imaging. The number of applications of AI in neuroradiology has also grown. This ...

Population modeling with machine learning can enhance measures of mental health.

GigaScience
BACKGROUND: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are ...

Brief Report: Neuroimaging Endophenotypes of Social Robotic Applications in Autism Spectrum Disorder.

Journal of autism and developmental disorders
A plethora of neuroimaging studies have focused on the discovery of potential neuroendophenotypes useful to understand the etiopathogenesis of autism and predict treatment response. Social robotics has recently been proposed as an effective tool to s...