AIMC Topic: Brain Neoplasms

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Medical Transformer: Universal Encoder for 3-D Brain MRI Analysis.

IEEE transactions on neural networks and learning systems
Transfer learning has attracted considerable attention in medical image analysis because of the limited number of annotated 3-D medical datasets available for training data-driven deep learning models in the real world. We propose Medical Transformer...

The G Protein-Coupled Receptor-Related Gene Signatures for Diagnosis and Prognosis in Glioblastoma: A Deep Learning Model Using RNA-Seq Data.

Asian Pacific journal of cancer prevention : APJCP
BACKGROUND: Glioblastoma (GBM) is the most aggressive cancer in the central nervous system in glial cells. Finding novel biomarkers in GBM offers numerous advantages that can contribute to early detection, personalized treatment, improved patient out...

A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation.

Scientific reports
The human-brain is a vital and complicated organ within the body. Identifying brain-related diseases can be challenging. Typically, Magnetic Resonance Imaging (MRI) scanning methods are used to gain insights of the protected regions in the body. Brai...

Quality assessment of critical and non-critical domains of systematic reviews on artificial intelligence in gliomas using AMSTAR II: A systematic review.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
INTRODUCTION: Gliomas are the most common primary malignant intraparenchymal brain tumors with a dismal prognosis. With growing advances in artificial intelligence, machine learning and deep learning models are being utilized for preoperative, intrao...

A comprehensive neuroimaging review of the primary and metastatic brain tumors treated with immunotherapy: current status, and the application of advanced imaging approaches and artificial intelligence.

Frontiers in immunology
Cancer immunotherapy has emerged as a novel clinical therapeutic option for a variety of solid tumors over the past decades. The application of immunotherapy in primary and metastatic brain tumors continues to grow despite limitations due to the phys...

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI.

Sensors (Basel, Switzerland)
Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnost...

Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images.

Scientific reports
Brain tumours (BT) affect human health owing to their location. Artificial intelligence (AI) is intended to assist in diagnosing and treating complex diseases by combining technologies like deep learning (DL), big data analytics, and machine learning...

Brain CT image classification based on mask RCNN and attention mechanism.

Scientific reports
Along with the computer application technology progress, machine learning, and block-chain techniques have been applied comprehensively in various fields. The application of machine learning, and block-chain techniques into medical image retrieval, c...

Brain MR-only workflow in clinical practice: A comparison among generators for quality assurance and patient positioning.

Journal of applied clinical medical physics
BACKGROUND AND PURPOSE: Routine quality control procedures are still required for sCT based on artificial intelligence (AI) to verify the performance of the generators. The aim of this study was to evaluate three generators based on AI or bulk densit...

Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.

Neurosurgery
Recent advancements in neuroimaging and machine learning have significantly improved our ability to diagnose and categorize isocitrate dehydrogenase (IDH)-wildtype glioblastoma, a disease characterized by notable tumoral heterogeneity, which is cruci...