AIMC Topic: Meningeal Neoplasms
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Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions.
Physics in medicine and biology
Apr 26, 2024
To address the challenge of meningioma grading, this study aims to investigate the potential value of peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics and deep learning techniques.The primary focus is on develo...
Harnessing generative artificial intelligence for meningioma prediction: a correspondence.
Neurosurgical review
Apr 23, 2024
Employing deep learning and transfer learning for accurate brain tumor detection.
Scientific reports
Mar 27, 2024
Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosi...
Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.
BMC medical imaging
Mar 5, 2024
BACKGROUND: This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentatio...
Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas.
Academic radiology
Feb 26, 2024
A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and...
Detection and classification of brain tumor using hybrid deep learning models.
Scientific reports
Dec 27, 2023
Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diag...
Meningioma consistency assessment based on the fusion of deep learning features and radiomics features.
European journal of radiology
Dec 5, 2023
PURPOSE: This study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency.
Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images.
World neurosurgery
Nov 28, 2023
OBJECTIVE: The primary aim of this research was to harness the capabilities of deep learning to enhance neurosurgical procedures, focusing on accurate tumor boundary delineation and classification. Through advanced diagnostic tools, we aimed to offer...
Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas.
Journal of computer assisted tomography
Nov 27, 2023
PURPOSE: This study aimed to investigate the effectiveness and practicality of using models like convolutional neural network and transformer in detecting and precise segmenting meningioma from magnetic resonance images.
Predicting meningioma grades and pathologic marker expression via deep learning.
European radiology
Oct 19, 2023
OBJECTIVES: To establish a deep learning (DL) model for predicting tumor grades and expression of pathologic markers of meningioma.
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