4.7 Article

Identification of glioblastoma molecular subtype and prognosis based on deep MRI features

Related references

Note: Only part of the references are listed.
Article Biochemical Research Methods

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

Ran Su et al.

BRIEFINGS IN BIOINFORMATICS (2020)

Article Biotechnology & Applied Microbiology

RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

Qiangguo Jin et al.

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY (2020)

Article Biochemical Research Methods

Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction

Ran Su et al.

BRIEFINGS IN BIOINFORMATICS (2020)

Article Biochemical Research Methods

Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

Ran Su et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2019)

Article Biochemical Research Methods

Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response

Ran Su et al.

METHODS (2019)

Article Computer Science, Artificial Intelligence

DUNet: A deformable network for retinal vessel segmentation

Qiangguo Jin et al.

KNOWLEDGE-BASED SYSTEMS (2019)

Article Clinical Neurology

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

P. Chang et al.

AMERICAN JOURNAL OF NEURORADIOLOGY (2018)

Article Computer Science, Interdisciplinary Applications

Prediction of survival with multi-scale radiomic analysis in glioblastoma patients

Ahmad Chaddad et al.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2018)

Article Multidisciplinary Sciences

A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

Jiangwei Lao et al.

SCIENTIFIC REPORTS (2017)

Article Radiology, Nuclear Medicine & Medical Imaging

Magnetic resonance imaging texture analysis classification of primary breast cancer

S. A. Waugh et al.

EUROPEAN RADIOLOGY (2016)

Review Oncology

Radiomics in glioblastoma: current status, challenges and potential opportunities

Shivali Narang et al.

TRANSLATIONAL CANCER RESEARCH (2016)

Article Multidisciplinary Sciences

Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features

Emmanuel Rios Velazquez et al.

SCIENTIFIC REPORTS (2015)

Article Oncology

The epidemiology of glioma in adults: a state of the science review

Quinn T. Ostrom et al.

NEURO-ONCOLOGY (2014)

Article Multidisciplinary Sciences

Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Chintan Parmar et al.

PLOS ONE (2014)

Article Radiology, Nuclear Medicine & Medical Imaging

Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features

Olivier Gevaert et al.

RADIOLOGY (2014)

Article Radiology, Nuclear Medicine & Medical Imaging

MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set

David A. Gutman et al.

RADIOLOGY (2013)

Review Cell Biology

Emerging insights into the molecular and cellular basis of glioblastoma

Gavin P. Dunn et al.

GENES & DEVELOPMENT (2012)

Article Radiology, Nuclear Medicine & Medical Imaging

Classification of Brain Tumor Type and Grade Using MRI Texture and Shape in a Machine Learning Scheme

Evangelia I. Zacharaki et al.

MAGNETIC RESONANCE IN MEDICINE (2009)

Article Computer Science, Artificial Intelligence

Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development

O. Sertel et al.

PATTERN RECOGNITION (2009)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)