High dimensional neuroimaging datasets and machine learning have been used to estimate and predict domain-specific cognition, but comparisons with simpler models composed of easy-to-measure variables are limited. Regularization methods in particular ...
Hallucinations in Parkinson's disease (PD) are disturbing and frequent non-motor symptoms and constitute a major risk factor for psychosis and dementia. We report a robotics-based approach applying conflicting sensorimotor stimulation, enabling the i...
The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is pa...
The extreme complexity of mammalian brains requires a comprehensive deconstruction of neuroanatomical structures. Scientists normally use a brain stereotactic atlas to determine the locations of neurons and neuronal circuits. However, different brain...
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2021
BACKGROUND: Doctors with various specializations and experience order brain computed tomography (CT) to rule out intracranial hemorrhage (ICH). Advanced artificial intelligence (AI) can discriminate subtypes of ICH with high accuracy.
BACKGROUND: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it...
BACKGROUND: Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors.
BACKGROUND: The automatic segmentation of brain tumour from MRI medical images is mainly covered in this review. Recently, state-of-the-art performance is provided by deep learning- based approaches in the field of image classification, segmentation,...
With the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation task is the structured organization of a dataset for preserving integrity and ensuring reusability. Th...
Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neu...
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