AI Medical Compendium Topic

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Multimodal Imaging

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Deep learning for whole-body medical image generation.

European journal of nuclear medicine and molecular imaging
BACKGROUND: Artificial intelligence (AI) algorithms based on deep convolutional networks have demonstrated remarkable success for image transformation tasks. State-of-the-art results have been achieved by generative adversarial networks (GANs) and tr...

Role of Machine Learning and Artificial Intelligence in Interventional Oncology.

Current oncology reports
PURPOSE OF REVIEW: The purpose of this review is to highlight the current role of machine learning and artificial intelligence and in the field of interventional oncology.

Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and treatment planning. In addition, multi-modal MR images can provide comp...

Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging.

Magnetic resonance in medicine
PURPOSE: The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron density. Based on our finding that longitudinal relaxation rate, or R , is associated with CT Hounsfi...

Deep Learning-Based Acute Ischemic Stroke Lesion Segmentation Method on Multimodal MR Images Using a Few Fully Labeled Subjects.

Computational and mathematical methods in medicine
Acute ischemic stroke (AIS) has been a common threat to human health and may lead to severe outcomes without proper and prompt treatment. To precisely diagnose AIS, it is of paramount importance to quantitatively evaluate the AIS lesions. By adopting...

Machine learning-based multimodal prediction of language outcomes in chronic aphasia.

Human brain mapping
Recent studies have combined multiple neuroimaging modalities to gain further understanding of the neurobiological substrates of aphasia. Following this line of work, the current study uses machine learning approaches to predict aphasia severity and ...