Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

Convolutional neural networks (CNNs) constitute a widely used deep learning approach that
has frequently been applied to the problem of brain tumor diagnosis. Such techniques still …

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …

Data harmonization is an important method for combining or transforming data. To date
however, articles about data harmonization are field-specific and highly technical, making it …

Meningiomas are the most common primary intracranial tumor in adults and can be
associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro …

Background: Early diagnosis of brain diseases is very important. Brain disease classification
is a common and complex topic in biomedical engineering. Therefore, machine learning …

Objectives Diagnostic tools for voice disorders are lacking for primary care physicians.
Artificial intelligence (AI) tools may add to the armamentarium for physicians, decreasing the …

Simple Summary Uterine cervical cancer (CC) is a leading cause of cancer-related deaths in
women worldwide. Pelvic magnetic resonance imaging (MRI) allows the assessment of local …

Magnetoencephalography (MEG) provides crucial information in diagnosing focal epilepsy.
However, dipole estimation, a commonly used analysis method for MEG, can be time …

Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis
pipelines. Machine-learning-based brain MR image segmentation methods are among the …