AI Medical Compendium Topic:
Brain Neoplasms

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Automatic detection and segmentation of multiple brain metastases on magnetic resonance image using asymmetric UNet architecture.

Physics in medicine and biology
Detection of brain metastases is a paramount task in cancer management due both to the number of high-risk patients and the difficulty of achieving consistent detection. In this study, we aim to improve the accuracy of automated brain metastasis (BM)...

Brain tumor detection and multi-classification using advanced deep learning techniques.

Microscopy research and technique
A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, a...

The Co-Pilot Project: An International Neurosurgical Collaboration in Ukraine.

World neurosurgery
OBJECTIVE: We aim to provide a thorough description of the efforts and outcomes of the Co-Pilot Project in Ukraine, which facilitates neurosurgical collaboration between American and Ukrainian physicians.

Knowledge transfer between brain lesion segmentation tasks with increased model capacity.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Convolutional neural networks (CNNs) have become an increasingly popular tool for brain lesion segmentation in recent years due to its accuracy and efficiency. However, CNN-based brain lesion segmentation generally requires a large amount of annotate...

Comparison of deep learning synthesis of synthetic CTs using clinical MRI inputs.

Physics in medicine and biology
There has been substantial interest in developing techniques for synthesizing CT-like images from MRI inputs, with important applications in simultaneous PET/MR and radiotherapy planning. Deep learning has recently shown great potential for solving t...

Feasibility of automated planning for whole-brain radiation therapy using deep learning.

Journal of applied clinical medical physics
PURPOSE: The purpose of this study was to develop automated planning for whole-brain radiation therapy (WBRT) using a U-net-based deep-learning model for predicting the multileaf collimator (MLC) shape bypassing the contouring processes.

Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis.

Contrast media & molecular imaging
PURPOSE: This study aimed to estimate the diagnostic accuracy of machine learning- (ML-) based radiomics in differentiating high-grade gliomas (HGG) from low-grade gliomas (LGG) and to identify potential covariates that could affect the diagnostic ac...

Improved Glioma Grading Using Deep Convolutional Neural Networks.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Accurate determination of glioma grade leads to improved treatment planning. The criterion standard for glioma grading is invasive tissue sampling. Recently, radiomic features have shown excellent potential in glioma-grade pre...

Analyzing magnetic resonance imaging data from glioma patients using deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical use of computational tools. The underlying technology to the vast majority of these tools are machin...

Estimating Local Cellular Density in Glioma Using MR Imaging Data.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which ce...