AIMC Topic: Meningeal Neoplasms
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Differentiating spinal pathologies by deep learning approach.
The spine journal : official journal of the North American Spine Society
Oct 4, 2023
BACKGROUND CONTEXT: Spinal pathologies are diverse in nature and, excluding trauma and degenerative diseases, includes infectious, neoplastic (either extradural or intradural), and inflammatory conditions. The preoperative diagnosis is made with clin...
TumorDetNet: A unified deep learning model for brain tumor detection and classification.
PloS one
Sep 27, 2023
Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment process and helps to save the lives of a large number of people worldwide. Because they are non-invasive and spare patients from having an unpleasant biopsy, ...
Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis.
World neurosurgery
Aug 12, 2023
BACKGROUND: Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional ap...
Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.
European radiology
Apr 13, 2023
OBJECTIVES: To establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation.
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques.
BMC medical informatics and decision making
Jan 23, 2023
BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and ...
Brain tumor classification based on neural architecture search.
Scientific reports
Nov 10, 2022
Brain tumor is a life-threatening disease and causes about 0.25 million deaths worldwide in 2020. Magnetic Resonance Imaging (MRI) is frequently used for diagnosing brain tumors. In medically underdeveloped regions, physicians who can accurately diag...
Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.
Scientific reports
Sep 14, 2022
Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter...
An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.
Tomography (Ann Arbor, Mich.)
Jul 26, 2022
A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis, as a minor human judgment can eventually cause severe consequences. Magnetic Resonance Image (MRI) ...
Editorial for "Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning".
Journal of magnetic resonance imaging : JMRI
Jul 1, 2022
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