Michael Berks

TL;DR: This work has built convolutional neural networks (CNN) to predict density VAS scores from full-field digital mammograms and shows promising results for cancer risk prediction and is comparable with human performance.

TL;DR: Evaluability is one of the major challenges in assessing nailfold capillaries, but the high intra- and inter-reliabilities suggest that overall image grade, capillary density and apex width have potential as outcome measures in longitudinal studies.

TL;DR: A fully automated system for extracting quantitative biomarkers from capillaroscopy images, using a layered machine learning approach, that reveals statistically significant differences between patients with (relatively benign) primary Raynaud's phenomenon, and those with potentially life-threatening systemic sclerosis.

TL;DR: Quantitative nailfold capillaroscopy, at least with a single observer, provides reliable outcome measures for clinical studies including randomised controlled trials, and within-operator image analysis and acquisition are reproducible.

TL;DR: This work adopts a discriminative learning approach based on a Dual-Tree Complex Wavelet representation and random forest classification that gives significantly better results than any of the other methods on the challenge of detecting curvilinear structure in mammograms.