Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks

[1]  U. Rajendra Acharya,et al.  Convolutional neural networks for multi-class brain disease detection using MRI images , 2019, Comput. Medical Imaging Graph..

[2]  U. Rajendra Acharya,et al.  Application of deep transfer learning for automated brain abnormality classification using MR images , 2019, Cognitive Systems Research.

[3]  F. Rybicki,et al.  American College of Radiology Appropriateness Criteria: Advancing Evidence-Based Imaging Practice. , 2019, Seminars in nuclear medicine.

[4]  Kawal S. Rhode,et al.  Training Deep Networks on Domain Randomized Synthetic X-ray Data for Cardiac Interventions , 2018, MIDL.

[5]  Sasank Chilamkurthy,et al.  Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study , 2018, The Lancet.

[6]  L. Sugrue,et al.  Hybrid 3D/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT , 2018, American Journal of Neuroradiology.

[7]  Rui Jiang,et al.  Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations , 2018, MICCAI.

[8]  J. Leung,et al.  Artificial intelligence and deep learning - Radiology's next frontier? , 2018, Clinical imaging.

[9]  Brandon K. Fornwalt,et al.  Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration , 2018, npj Digital Medicine.

[10]  El-Sayed M. El-Horbaty,et al.  Classification using deep learning neural networks for brain tumors , 2017, Future Computing and Informatics Journal.

[11]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.

[12]  Daguang Xu,et al.  Automatic Liver Segmentation Using an Adversarial Image-to-Image Network , 2017, MICCAI.

[13]  Richard D. White,et al.  Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging. , 2017, Radiology.

[14]  Liang Chen,et al.  Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks , 2017, NeuroImage: Clinical.

[15]  Muhammad Imran Razzak,et al.  Deep Learning for Medical Image Processing: Overview, Challenges and Future , 2017, ArXiv.

[16]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[17]  Guang-Zhong Yang,et al.  Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.

[18]  Vibha Vyas,et al.  A novel training algorithm for convolutional neural network , 2016, Complex & Intelligent Systems.

[19]  Chao Lu,et al.  Retrospective study , 2016, Medicine.

[20]  B. M. ter Haar Romeny,et al.  Automated detection of cerebral microbleeds in patients with Traumatic Brain Injury , 2016, NeuroImage: Clinical.

[21]  Muhammad Faisal Siddiqui,et al.  An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification , 2015, PloS one.

[22]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[23]  J. Angel Arul Jothi,et al.  Automatic Classification of Brain MRI Images Using SVM and Neural Network Classifiers , 2014, ISI.

[24]  Adrian F Hernandez,et al.  Time to treatment with intravenous tissue plasminogen activator and outcome from acute ischemic stroke. , 2013, JAMA.

[25]  Sudeb Das,et al.  Brain Mr Image Classification Using Multiscale Geometric Analysis of Ripplet , 2013 .

[26]  Yudong Zhang,et al.  AN MR BRAIN IMAGES CLASSIFIER VIA PRINCIPAL COMPONENT ANALYSIS AND KERNEL SUPPORT , 2012 .

[27]  William A Edelstein,et al.  MRI: time is dose--and money and versatility. , 2010, Journal of the American College of Radiology : JACR.

[28]  Abdel-Badeeh M. Salem,et al.  Hybrid intelligent techniques for MRI brain images classification , 2010, Digit. Signal Process..

[29]  Ale Algra,et al.  Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis , 2010, The Lancet Neurology.

[30]  S M Davis,et al.  Hematoma growth is a determinant of mortality and poor outcome after intracerebral hemorrhage , 2006, Neurology.

[31]  Lalit M. Patnaik,et al.  Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network , 2006, Biomed. Signal Process. Control..

[32]  Niels Hjort,et al.  Ischemic injury detected by diffusion imaging 11 minutes after stroke , 2005, Annals of neurology.

[33]  E Godehardt,et al.  Exclusion of brain lesions: is MR contrast medium required after a negative fluid-attenuated inversion recovery sequence? , 2004, The British journal of radiology.

[34]  H. Eisenberg,et al.  Initial CT findings in 753 patients with severe head injury. A report from the NIH Traumatic Coma Data Bank. , 1990, Journal of neurosurgery.

[35]  W. Knaus,et al.  Study design: data collection , 1989 .