Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives
A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting
pressure on the healthy parts of the brain, it can lead to significant health problems …
pressure on the healthy parts of the brain, it can lead to significant health problems …
MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction
Background Unsupervised learning can discover various unseen abnormalities, relying on
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …
Application of deep learning to ischemic and hemorrhagic stroke computed tomography and magnetic resonance imaging
Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been
applied not only to the “downstream” side such as lesion detection, treatment decision …
applied not only to the “downstream” side such as lesion detection, treatment decision …
Generalizability of machine learning models: quantitative evaluation of three methodological pitfalls
Purpose To investigate the impact of the following three methodological pitfalls on model
generalizability:(a) violation of the independence assumption,(b) model evaluation with an …
generalizability:(a) violation of the independence assumption,(b) model evaluation with an …
Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images
Abstract Background and Objective The purpose of the present study was to investigate low-
shot deep learning models applied to conjunctival melanoma detection using a small …
shot deep learning models applied to conjunctival melanoma detection using a small …
Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis
Objectives This systematic review and meta-analysis aimed to assess the stroke detection
performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and …
performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and …
[HTML][HTML] Image translation for medical image generation: Ischemic stroke lesion segmentation
M Platscher, J Zopes, C Federau - Biomedical Signal Processing and …, 2022 - Elsevier
Deep learning based disease detection and segmentation algorithms promise to improve
many clinical processes. However, such algorithms require vast amounts of annotated …
many clinical processes. However, such algorithms require vast amounts of annotated …
Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging
Stroke is a leading cause of death and disability. The ability to quickly identify the presence
of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important …
of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important …
Diagnostic test accuracy study of a commercially available deep learning algorithm for ischemic lesion detection on brain MRIs in suspected stroke patients from a non …
Purpose To estimate the ability of a commercially available artificial intelligence (AI) tool to
detect acute brain ischemia on Magnetic Resonance Imaging (MRI), compared to an …
detect acute brain ischemia on Magnetic Resonance Imaging (MRI), compared to an …
Improving ischemic stroke care with MRI and deep learning artificial intelligence
Advanced magnetic resonance imaging has been used as selection criteria for both acute
ischemic stroke treatment and secondary prevention. The use of artificial intelligence, and in …
ischemic stroke treatment and secondary prevention. The use of artificial intelligence, and in …