Google Inc.

Deepmind Technologies Limited

Pearse A Keane

Jeffrey De Fauw

Trevor Back

Mustafa Suleyman

Olaf Ronneberger

Samuel Blackwell

Xavier Glorot

Bernardino Romera-Paredes

Julien Robert Michel Cornebise

Harry Askham

Stanislav Nikolov

Nenad Tomasev

Joseph R Ledsam

Balaji Lakshminarayanan

Annette Ada Nkechinyere Obika

Jim Huibrecht Winkens

Jason Yim

Reena Kumari Chopra

Terry Spitz

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 a computer storage medium, for generating a final progression score characterizing a likelihood that a state of a medical condition affecting eye tissue will progress to a target state in a future interval of time. In one aspect, a method comprises: obtaining: (i) an input image of eye tissue captured using an imaging modality, and (ii) a segmentation map of the eye tissue in the input image into a plurality of tissue types; providing the input image to each of one or more first classification neural networks to obtain a respective first progression score from each first classification neural network; providing the segmentation map to each of one or more second classification neural networks to obtain a respective second progression score from each second classification neural network; and generating the final progression score based on the first and second progression scores.

Predicting disease progression from tissue images and tissue segmentation maps

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London, United Kingdom

Pearse A Keane