AIMC Topic:
Magnetic Resonance Imaging
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Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 3, 2023
Attention Deficit Hyperactivity Disorder (ADHD) is a type of mental health disorder that can be seen from children to adults and affects patients' normal life. Accurate diagnosis of ADHD as early as possible is very important for the treatment of pat...
A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging.
Diagnostic and interventional imaging
Apr 2, 2023
PURPOSE: The purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predict the MRI Assessment of SpondyloArthritis International Society (ASAS) definition of active sacroili...
A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI.
Medical physics
Apr 1, 2023
BACKGROUND: Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires specialized equipment and exogenous contrast, wh...
Applications of Artificial Intelligence in the Automatic Diagnosis of Focal Liver Lesions: A Systematic Review.
Journal of gastrointestinal and liver diseases : JGLD
Apr 1, 2023
BACKGROUND AND AIMS: Focal liver lesions (FLLs) are defined as abnormal solid or liquid masses differentiated from normal liver, frequently being clinically asymptomatic. The aim of this systematic review is to provide a comprehensive overview of cur...
Effect of Deep Learning Reconstruction on Respiratory-triggered T2-weighted MR Imaging of the Liver: A Comparison between the Single-shot Fast Spin-echo and Fast Spin-echo Sequences.
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Mar 29, 2023
PURPOSE: To compare the effects of deep learning reconstruction (DLR) on respiratory-triggered T2-weighted MRI of the liver between single-shot fast spin-echo (SSFSE) and fast spin-echo (FSE) sequences.
Decoding reappraisal and suppression from neural circuits: A combined supervised and unsupervised machine learning approach.
Cognitive, affective & behavioral neuroscience
Mar 28, 2023
Emotion regulation is a core construct of mental health and deficits in emotion regulation abilities lead to psychological disorders. Reappraisal and suppression are two widely studied emotion regulation strategies but, possibly due to methodological...
Detecting individuals with severe mental illness using artificial intelligence applied to magnetic resonance imaging.
EBioMedicine
Mar 28, 2023
BACKGROUND: Identifying individuals at risk for severe mental illness (SMI) is crucial for prevention and early intervention strategies. While MRI shows potential for case identification even before illness onset, no practical model for mental health...
Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer.
Journal of digital imaging
Mar 27, 2023
The objective of this study is to develop a radiomic signature constructed from deep learning features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. Preoperative magnetic resonance imaging data from...
Editorial for "Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR Images".
Journal of magnetic resonance imaging : JMRI
Mar 25, 2023
Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images.
Journal of magnetic resonance imaging : JMRI
Mar 25, 2023
BACKGROUND: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear.
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