AI Medical Compendium Topic:
Brain Neoplasms

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Information Geometric Approaches for Patient-Specific Test-Time Adaptation of Deep Learning Models for Semantic Segmentation.

IEEE transactions on medical imaging
The test-time adaptation (TTA) of deep-learning-based semantic segmentation models, specific to individual patient data, was addressed in this study. The existing TTA methods in medical imaging are often unconstrained, require anatomical prior inform...

Hierarchical Multi-Class Group Correlation Learning Network for Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Hierarchical approaches have been tremendously successful at multi-label segmentation. However, it has been shown they may seriously suffer from the problem of only imposing constraints on shallow layers while ignoring deep relationships in the label...

Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation ...

Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.

Neuro-oncology
BACKGROUND: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.

Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.

Journal of computer assisted tomography
OBJECTIVE: The aim of this study was to evaluate various radiomics-based machine learning classification models using the apparent diffusion coefficient (ADC) and cerebral blood flow (CBF) maps for differentiating between low-grade gliomas (LGGs) and...

Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning.

Scientific reports
This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by integrating advanced machine learning (ML) techniques with comprehensive imaging and clinical data. ...

Fusion-Brain-Net: A Novel Deep Fusion Model for Brain Tumor Classification.

Brain and behavior
PROBLEM: Brain tumors are among the most prevalent and lethal diseases. Early diagnosis and precise treatment are crucial. However, the manual classification of brain tumors is a laborious and complex task.

Brain multi modality image inpainting via deep learning based edge region generative adversarial network.

Technology and health care : official journal of the European Society for Engineering and Medicine
A brain tumor (BT) is considered one of the most crucial and deadly diseases in the world, as it affects the central nervous system and its main functions. Headaches, nausea, and balance problems are caused by tumors pressing on nearby brain tissue a...

Advancing brain tumor detection and classification in Low-Dose CT images using the innovative multi-layered deep neural network model.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundEffective brain tumour therapy and better patient outcomes depend on early tumour diagnosis. Accurate diagnosis can be hampered by traditional imaging techniques' frequent struggles with low resolution and noise, especially in Low Dose CT s...

Unsupervised Deep Learning for Blood-Brain Barrier Leakage Detection in Diffuse Glioma Using Dynamic Contrast-enhanced MRI.

Radiology. Artificial intelligence
Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier leakage detection using dynamic contrast-enhanced MRI, without requiring pharmacokinetic models and arterial input function estimation. Materials and Met...