Greg S. Corrado

TL;DR: A new wave of studies has confronted the conundrum of internal decision variables more directly by leveraging quantitative behavioral models and allowed researchers to localize neural subsystems that encode hidden decision variables related to free choice and to study these variables under dynamic conditions.

TL;DR: The deep learning system warrants evaluation as an assistive tool for improving prostate cancer diagnosis and treatment decisions, especially where subspecialist expertise is unavailable.

TL;DR: This work shows the potential of automated non-invasive anemia screening based on fundus images, particularly in diabetic patients, who may have regular retinal imaging and are at increased risk of further morbidity and mortality from anemia.

TL;DR: SMILY as mentioned in this paper is a reverse image search tool for histopathology images that retrieves similar histologic features, organ site, and prostate cancer Gleason grade compared with the original query.

TL;DR: Machine-learning algorithms trained with retinal fundus images, with subject metadata or with both data types, predict haemoglobin concentration with mean absolute errors lower than 0.75 and anaemia with areas under the curve in the range of 0.74–0.89.