Machine Learning in Neurooncology Imaging: From Study Request to Diagnosis and Treatment
- 1 January 2019
- journal article
- review article
- Published by American Roentgen Ray Society in American Journal of Roentgenology
- Vol. 212 (1) , 52-56
Abstract
OBJECTIVE. Machine learning has potential to play a key role across a variety of medical imaging applications. This review seeks to elucidate the ways in which machine learning can aid and enhance diagnosis, treatment, and follow-up in neurooncology. CONCLUSION. Given the rapid pace of development in machine learning over the past several years, a basic proficiency of the key tenets and use cases in the field is critical to assessing potential opportunities and challenges of this exciting new technology.Keywords
This publication has 44 references indexed in Scilit:
- Brain Tumor Segmentation Using Convolutional Neural Networks in MRI ImagesIEEE Transactions on Medical Imaging, 2016
- q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans2015
- 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data2015
- Brain tumor grading based on Neural Networks and Convolutional Neural Networks2015
- Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks2015
- Evolution of DNA repair defects during malignant progression of low-grade gliomas after temozolomide treatmentActa Neuropathologica, 2015
- Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learningMedical Image Analysis, 2013
- Comparison of Manual and Automatic Section Positioning of Brain MR ImagesRadiology, 2006
- Improved Detection of Lung Nodules on Chest Radiographs Using a Commercial Computer-Aided Diagnosis SystemAmerican Journal of Roentgenology, 2004
- Computer-Aided Detection (CAD) in Screening Mammography: Sensitivity of Commercial CAD Systems for Detecting Architectural DistortionAmerican Journal of Roentgenology, 2003