We present a fully-automated deep learning-based system for temporalis muscle quantification, a skeletal muscle mass surrogate on MRI head. Methods: MRI scans ...
In patients with brain tumors, measurement of the thickness of the temporalis muscle acts as a proxy. We present a fully-automated deep learning-based system ...
Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma · Medicine, Computer Science. British Journal of ...
Jan 23, 2024 · UKBB: A fully automated deep learning pipeline to assess muscle mass in brain tumor patients. 25 views · 10 months ago ...more ...
Temporalis muscle cross-sectional area can be rapidly and accurately assessed from 3D MRI brain scans using a deep learning-based system in a fully automated ...
Missing: tumor | Show results with:tumor
A fully automated deep learning method was developed to segment brain tumors into their subcomponents, which achieved high prediction accuracy.
Missing: pipeline muscle
In patients with brain tumors, measurement of the thickness of the temporalis muscle acts as a proxy. We present a fully-automated deep learning-based system ...
People also ask
How to detect brain tumors using deep learning?
[62] developed a multi-modal fusion deep learning model for brain tumor grading. Their method combined features from MRI images and clinical data and achieved high accuracy in classifying tumor grades, demonstrating the potential of deep learning in aiding clinical decision-making.
How AI is used in brain tumor detection?
AI shows significant promise in diagnosis, prognosis, and treatment planning by effectively detecting and classifying brain tumors from medical images. Through radiomic, pathomic, and genomic analyses, AI contributes to precise tumor characterization.
What not to do with a brain tumor?
Brain tumor patients should avoid:
Processed Foods: These often contain unhealthy fats, excessive sugars, and artificial additives that can contribute to inflammation and other health issues.
Sugary Foods and Drinks: High sugar intake can lead to weight gain, increased inflammation, and a weakened immune system.
What is the CNN model for brain tumor detection?
The first CNN model achieves an impressive detection accuracy of 99.53% for brain tumors. The second CNN model, with an accuracy of 93.81%, proficiently categorizes brain tumors into five distinct types: normal, glioma, meningioma, pituitary, and metastatic.
Nov 30, 2021 · We present a deep learning-based system for quantification of temporalis muscle, a surrogate for skeletal muscle mass, and assess its prognostic value in ...
Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration. Sibaji Gaj,Brendan L Eck,Dongxing Xie ...
A fully automated deep learning method was developed to segment brain tumors into their subcomponents, which achieved high prediction accuracy.
Missing: pipeline muscle