AIMC Topic: Glioma
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Automated brain histology classification using machine learning.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
May 31, 2019
Brain and breast tumors cause significant morbidity and mortality worldwide. Accurate and expedient histological diagnosis of patients' tumor specimens is required for subsequent treatment and prognostication. Currently, histology slides are visually...
Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks.
Genomics
May 29, 2019
BACKGROUND: Glioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mec...
Brain tumor detection using statistical and machine learning method.
Computer methods and programs in biomedicine
May 17, 2019
BACKGROUND AND OBJECTIVE: Brain tumor occurs because of anomalous development of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumor. Earlier brain tumo...
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.
Computers in biology and medicine
May 3, 2019
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentati...
A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks.
Sensors (Basel, Switzerland)
Apr 28, 2019
Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neur...
Multimodal brain tumor image segmentation using WRN-PPNet.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 23, 2019
Tumor segmentation is of great importance for diagnosis and prognosis of brain cancer in medical field. Because of the noise, inhomogeneous gray, diversity of tissue, bias among modalities, and the fuzzy boundaries between tumor and adjacent tissues ...
A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas.
NeuroImage. Clinical
Apr 22, 2019
OBJECTIVES: To investigate the association between proton magnetic resonance spectroscopy (H-MRS) metabolic features and the grade of gliomas, and to establish a machine-learning model to predict the glioma grade.
Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.
Journal of medical systems
Mar 21, 2019
Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification f...
A novel end-to-end brain tumor segmentation method using improved fully convolutional networks.
Computers in biology and medicine
Mar 18, 2019
Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an active research topic in medical image analysis since it provides doctors with meaningful and reliable quantitative information in diagnosing and monitoring neurolo...
Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas.
Clinical radiology
Mar 5, 2019
This paper describes state-of-the-art methods for molecular biomarker prediction utilising magnetic resonance imaging. This review paper covers both classical machine learning approaches and deep learning approaches to identifying the predictive feat...
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