AI Medical Compendium Topic:
Brain Neoplasms

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Development and validation of a machine learning algorithm for predicting diffuse midline glioma, H3 K27-altered, H3 K27 wild-type high-grade glioma, and primary CNS lymphoma of the brain midline in adults.

Journal of neurosurgery
OBJECTIVE: Preoperative diagnosis of diffuse midline glioma, H3 K27-altered (DMG-A) and midline high-grade glioma without H3 K27 alteration (DMG-W), as well as midline primary CNS lymphoma (PCNSL) in adults, is challenging but crucial. The aim of thi...

Reinforcement learning using Deep networks and learning accurately localizes brain tumors on MRI with very small training sets.

BMC medical imaging
BACKGROUND: Supervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets; (2) It is non-generalizable; and (3) It lacks explainability and intuition. It has recently been proposed t...

DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning.

Acta neurochirurgica
PURPOSE: Volumetric assessments, such as extent of resection (EOR) or residual tumor volume, are essential criterions in glioma resection surgery. Our goal is to develop and validate segmentation machine learning models for pre- and postoperative mag...

Automated brain tumour segmentation from multi-modality magnetic resonance imaging data based on new particle swarm optimisation segmentation method.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Segmentation of brain tumours is a complex problem in medical image processing and analysis. It is a time-consuming and error-prone task. Therefore, computer-aided detection systems need to be developed to decrease physicians' workload an...

Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools.

Computers in biology and medicine
BACKGROUND: Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients' lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients s...

Deep learning architecture with transformer and semantic field alignment for voxel-level dose prediction on brain tumors.

Medical physics
PURPOSE: The use of convolution neural networks (CNN) to accurately predict dose distributions can accelerate intensity-modulated radiation therapy (IMRT) planning. The purpose of our study is to develop a novel deep learning architecture for precise...

Efficient framework for brain tumor detection using different deep learning techniques.

Frontiers in public health
The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals...

A Bibliometric Review: Brain Tumor Magnetic Resonance Imagings Using Different Convolutional Neural Network Architectures.

World neurosurgery
BACKGROUND: Numerous scientists and researchers have been developing advanced procedures and methods for diagnosing the kind and phase of a human tumor. Brain tumors, which are neoplastic and abnormal developments of brain cells, are one of the most ...

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Sensors (Basel, Switzerland)
Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possib...