Google Inc.

Deepmind Technologies Limited

Xavier Glorot

Trevor Back

Harry Askham

Nenad Tomasev

Mustafa Suleyman

Olaf Ronneberger

Jeffrey De Fauw

Samuel Blackwell

Shakir Mohamed

Julien Robert Michel Cornebise

Bernardino Romera-Paredes

Stanislav Nikolov

Joseph R Ledsam

Pearse A Keane

Alexander Lerchner

Irina Higgins

Balaji Lakshminarayanan

Loic Matthey-de-l'Endroit

Arka Tilak Pal

Jack William Rae

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

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a variational auto-encoder (VAE) to generate disentangled latent factors on unlabeled training images. In one aspect, a method includes receiving the plurality of unlabeled training images, and, for each unlabeled training image, processing the unlabeled training image using the VAE to determine the latent representation of the unlabeled training image and to generate a reconstruction of the unlabeled training image in accordance with current values of the parameters of the VAE, and adjusting current values of the parameters of the VAE by optimizing a loss function that depends on a quality of the reconstruction and also on a degree of independence between the latent factors in the latent representation of the unlabeled training image.

Training variational autoencoders to generate disentangled latent factors

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Montreal, Canada

Xavier Glorot