AIMC Topic: Magnetic Resonance Imaging
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A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma.
Computer methods and programs in biomedicine
Jan 21, 2022
BACKGROUND AND OBJECTIVE: Craniopharyngioma is a kind of benign brain tumor in histography. However, it might be clinically aggressive and have severe manifestations, such as increased intracranial pressure, hypothalamic-pituitary dysfunction, and vi...
Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals.
PloS one
Jan 21, 2022
Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrat...
Beyond the : Deep Learning-Enabled Virtual Postcontrast Imaging for Neurooncology.
AJR. American journal of roentgenology
Jan 19, 2022
A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor.
Brain imaging and behavior
Jan 19, 2022
A supervised CNN Deep net classifier is proposed for the detection, classification and diagnosis of meningioma brain tumor using deep learning approach. This proposed method includes preprocessing, classification, and segmentation of the primary occu...
Deep learning-based convolutional neural network for intramodality brain MRI synthesis.
Journal of applied clinical medical physics
Jan 19, 2022
PURPOSE: The existence of multicontrast magnetic resonance (MR) images increases the level of clinical information available for the diagnosis and treatment of brain cancer patients. However, acquiring the complete set of multicontrast MR images is n...
Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation.
Physics in medicine and biology
Jan 19, 2022
Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could becom...
Deep learning classification of inverted papilloma malignant transformation using 3D convolutional neural networks and magnetic resonance imaging.
International forum of allergy & rhinology
Jan 18, 2022
BACKGROUND: Distinguishing benign inverted papilloma (IP) tumors from those that have undergone malignant transformation to squamous cell carcinoma (IP-SCC) is important but challenging to do preoperatively. Magnetic resonance imaging (MRI) can help ...
Classification of rotator cuff tears in ultrasound images using deep learning models.
Medical & biological engineering & computing
Jan 18, 2022
Rotator cuff tears (RCTs) are one of the most common shoulder injuries, which are typically diagnosed using relatively expensive and time-consuming diagnostic imaging tests such as magnetic resonance imaging or computed tomography. Deep learning algo...
Predicting individual traits from unperformed tasks.
NeuroImage
Jan 18, 2022
Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracte...
Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.
Radiology
Jan 18, 2022
Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the foundation for downstream AI tasks. However, supervised training requires many image annotations, and per...
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