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James M. Brown

Researcher at University of Lincoln

Publications -  72
Citations -  5427

James M. Brown is an academic researcher from University of Lincoln. The author has contributed to research in topics: Retinopathy of prematurity & Deep learning. The author has an hindex of 22, co-authored 65 publications receiving 3907 citations. Previous affiliations of James M. Brown include University of Birmingham & Medical Research Council.

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
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High-throughput discovery of novel developmental phenotypes

Mary E. Dickinson, +85 more
- 22 Sep 2016 - 
TL;DR: It is shown that human disease genes are enriched for essential genes, thus providing a dataset that facilitates the prioritization and validation of mutations identified in clinical sequencing efforts and reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background.
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Etiology of human breast cancer: a review.

Brian MacMahon, +2 more
- 01 Jan 1973 - 
TL;DR: The hypothesis that breast cancer risk is related to estrogen metabolism during the first few years after menarche is the hypothesis most compatible with all the major epidemiological features of the disease and is virtually the only acceptable explanation.
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Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

James M. Brown, +11 more
- 01 Jul 2018 - 
TL;DR: This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts, which has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.
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Distributed deep learning networks among institutions for medical imaging.

Ken Chang, +8 more
- 01 Aug 2018 - 
TL;DR: It is shown that distributing deep learning models is an effective alternative to sharing patient data, and this finding has implications for any collaborative deep learning study.