Journal of neurointerventional surgery
Jan 21, 2021
Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on v...
The detailed anatomical information of the brain provided by 3D magnetic resonance imaging (MRI) enables various neuroscience research. However, due to the long scan time for 3D MR images, 2D images are mainly obtained in clinical environments. The p...
Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advant...
BACKGROUND: No large dataset-derived standard has been established for normal or pathologic human cerebral ventricular and cranial vault volumes. Automated volumetric measurements could be used to assist in diagnosis and follow-up of hydrocephalus or...
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 ...
Increasingly large MRI neuroimaging datasets are becoming available, including many highly multi-site multi-scanner datasets. Combining the data from the different scanners is vital for increased statistical power; however, this leads to an increase ...
AJNR. American journal of neuroradiology
Dec 24, 2020
BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is an important event that is diagnosed on head NCCT. Increased NCCT utilization in busy hospitals may limit timely identification of ICH. RAPID ICH is an automated hybrid 2D-3D convolutional neur...
AJNR. American journal of neuroradiology
Dec 17, 2020
BACKGROUND AND PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the change in acute or subacute ischemic strokes detected on CT or MR imaging during the pandemic using natu...
The classification of Alzheimer's disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various po...
One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the class of interest is relatively low, when compared to the rest of the data. This study aims to apply ove...