James Brown

Publications

• Artificial intelligence in retinopathy of prematurity: development of a fully automated deep convolutional neural network (DeepROP) for plus disease diagnosis
• Automated Computer-Based Image Analysis in Monitoring Disease Progression for Retinopathy of Prematurity
• Artificial intelligence in retinopathy of prematurity: identification of clinically significant retinal vascular findings using computer-based image analysis
• Artificial intelligence in retinopathy of prematurity (ROP): diagnostic performance of a supervised machine learning system (i-ROP ASSIST)
• Distributed deep learning networks among institutions for medical imaging
• Deep learning for image quality assessment of fundus images in retinopathy of prematurity
• Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity
• Viscerotoxic Brain Infarcts: The Results of Heart-Brain Interactions Study
• Deep feature transfer between localization and segmentation tasks
• MRI changes in patients with newly diagnosed glioblastoma treated as part of a Phase II trial with bavituximab, radiation, and temozolomide (P1. 6-003)
• Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement
• Classification and comparison via neural networks
• Monitoring Disease Progression with a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning
• Variability in Plus Disease Identified Using a Deep Learning-Based Retinopathy of Prematurity Severity Scale
• Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity
• Improved interpretability for computer-aided severity assessment of retinopathy of prematurity
• A bioimage informatics platform for high-throughput embryo phenotyping
• Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity
• Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture
• Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage
• DeepNeuro: an open-source deep learning toolbox for neuroimaging
• Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks
• CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images
• Improved interpretability for computer-aided severity assessment of retinopathy of prematurity
• Automated diagnosis of plus disease in retinopathy of prematurity using deep learning
• Application of a Quantitative Image Analysis Scale Using Deep Learning for Detection of Clinically Significant ROP
• Classification and comparison via neural networks
• Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging
• Supplementary Data 1 from Bavituximab Decreases Immunosuppressive Myeloid-Derived Suppressor Cells in Newly Diagnosed Glioblastoma Patients
• Data from Bavituximab Decreases Immunosuppressive Myeloid-Derived Suppressor Cells in Newly Diagnosed Glioblastoma Patients
• Supplementary Data 1 from Bavituximab Decreases Immunosuppressive Myeloid-Derived Suppressor Cells in Newly Diagnosed Glioblastoma Patients
• Bavituximab Decreases Immunosuppressive Myeloid-Derived Suppressor Cells in Newly Diagnosed Glioblastoma Patients
• Supplementary Data 1 from Bavituximab Decreases Immunosuppressive Myeloid-Derived Suppressor Cells in Newly Diagnosed Glioblastoma Patients
• Data from Bavituximab Decreases Immunosuppressive Myeloid-Derived Suppressor Cells in Newly Diagnosed Glioblastoma Patients
• Supplementary Data 1 from Bavituximab Decreases Immunosuppressive Myeloid-Derived Suppressor Cells in Newly Diagnosed Glioblastoma Patients
• Data from Bavituximab Decreases Immunosuppressive Myeloid-Derived Suppressor Cells in Newly Diagnosed Glioblastoma Patients
• Distributed deep learning networks among institutions for medical imaging
• Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity
• Anatomical DCE-MRI phantoms generated from glioma patient data
• Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks
• Plus disease in retinopathy of prematurity: Convolutional neural network performance using a combined neural network and feature extraction approach
• DeepNeuro: an open-source deep learning toolbox for neuroimaging
• Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bidimensional measurement
• Evaluation of a Deep Learning–Derived Quantitative Retinopathy of Prematurity Severity Scale
• A Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning to Monitor Disease Regression after Treatment
• Dual-Stream Spatiotemporal Networks with Feature Sharing for Monitoring Animals in the Home Cage
• Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks
• Aggressive Posterior Retinopathy of Prematurity
• Radiomics Repeatability Pitfalls in a Scan-Rescan MRI Study of Glioblastoma
• LAMA: automated image analysis for the developmental phenotyping of mouse embryos
• Monitoring response to treatment in severe retinopathy of prematurity using a deep learning based quantitative severity scale
• Risk assessment in retinopathy of prematurity: improvement of clinical models using automated image analysis
• Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
• High-resolution medical image synthesis using progressively grown generative adversarial networks
• NIMG-63. ADVANCED IMAGING FOR ASSESSING VOLUMETRIC RESPONSES IN BRAIN METASTASES TREATED WITH CHECKPOINT BLOCKADE
• NCOG-04. EFFECTS OF PROTON RADIATION ON BRAIN STRUCTURE AND FUNCTION IN LOW GRADE GLIOMA
• Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
• Detection and characterisation of bone destruction in murine rheumatoid arthritis using statistical shape models
• Disease model discovery from 3,328 gene knockouts by the International Mouse Phenotyping Consortium
• Comparative visualization of genotype-phenotype relationships
• ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI
• Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning
• 3D articulated registration of the mouse hind limb for bone morphometric analysis in rheumatoid arthritis
• 3D Articulated Registration of the Mouse Hind Limb for Bone Morphometric Analysis in Rheumatoid Arthritis
• Micro-CT analysis of bone destruction in mouse models of rheumatoid arthritis
• A mouse informatics platform for phenotypic and translational discovery
• A bioimage informatics platform for high-throughput embryo phenotyping
• Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning
• Articulated statistical shape models for the analysis of bone destruction in mouse models of rheumatoid arthritis
• Corrigendum: Comparative visualization of genotype-phenotype relationships
• Comparative visualization of genotype-phenotype relationships
• High-throughput discovery of novel developmental phenotypes
• Analysis of 3D embryo data for the International Mouse Phenotyping Consortium
• Detection and characterisation of bone destruction in murine rheumatoid arthritis using statistical shape models
• Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium
• Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
• Abstracts of papers presented at the 26th Genetics Society's Mammalian Genetics and Development Workshop held at the Institute of Child Health, University College London on 20th November 2015.
• High Throughput Imaging and Phenotyping of Homozygous Lethal Mouse Lines at MRC Harwell
• Sequential neural networks for biologically-informed glioma segmentation
• ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI
• How is plus disease diagnosed in ROP? Insights from a deep learning computer-based image analysis system with occlusion analysis
• Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation
• Erratum : Comparative visualization of genotype-phenotype relationships (Nat. Methods (2015) 12:698-699)
• Sequential neural networks for biologically informed glioma segmentation
• A mouse informatics platform for phenotypic and translational discovery
• High-throughput discovery of novel developmental phenotypes