Jeffrey De Fauw

TL;DR: A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.

TL;DR: A robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening and using a combination of AI and human inputs could help to improve screening efficiency.

TL;DR: This work revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations which make the variability in natural signals more predictable, and produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset.

TL;DR: A generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses and reproduces the possible segmentation variants as well as the frequencies with which they occur significantly better than published approaches.