Google Inc.

Deepmind Technologies Limited

Nenad Tomasev

Trevor Back

Harry Askham

Xavier Glorot

Mustafa Suleyman

Olaf Ronneberger

Jeffrey De Fauw

Samuel Blackwell

Julien Robert Michel Cornebise

Bernardino Romera-Paredes

Stanislav Nikolov

Joseph R Ledsam

Pearse A Keane

Balaji Lakshminarayanan

Jack William Rae

Shakir Mohamed

Clemens Ludwig Meyer

Ivan Protsyuk

Suman Ravuri

Andre Saraiva Nobre Dos Santos

Anne Mottram

Michal Zielinski

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final classification output for an image of eye tissue. The image is provided as input to each of one or more segmentation neural networks to obtain one or more segmentation maps of the eye tissue in the image. A respective classification input is generated from each of the segmentation maps. For each of the segmentation maps, the classification input for the segmentation map is provided as input to each of one or more classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network. A final classification output for the image is generated from the respective classification outputs for each of the segmentation maps.

Generalizable medical image analysis using segmentation and classification neural networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future adverse health events using neural networks. One of the methods includes receiving electronic health record data for a patient; generating, from the electronic health record data, an input sequence comprising a respective feature representation at each of a plurality of time window time steps, comprising, for each time window time step: determining, for each of the possible numerical features, whether the numerical feature occurred during the time window; and generating, for each of the possible numerical features, one or more presence features that identify whether the numerical feature occurred during the time window; and processing the input sequence using a neural network to generate a neural network output that characterizes a predicted likelihood that an adverse health event will occur to the patient.

Prediction of future adverse health events using neural networks by pre-processing input sequences to include presence features

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London, United Kingdom

Nenad Tomasev