Google Inc.

Deepmind Technologies Limited

Jeffrey De Fauw

Trevor Back

Samuel Blackwell

Olaf Ronneberger

Harry Askham

Bernardino Romera-Paredes

Stanislav Nikolov

Joseph R Ledsam

Pearse A Keane

Mustafa Suleyman

Xavier Glorot

Julien Robert Michel Cornebise

Nenad Tomasev

Balaji Lakshminarayanan

Clemens Ludwig Meyer

Cian Hughes

Shravya Shetty

Scott McKinney

Marcin Sieniek

Varun Godbole

Jim Huibrecht Winkens

Annette Ada Nkechinyere Obika

Jason Yim

Natasha Antropova

Christopher Kelly

Jonathan Godwin

Terry Spitz

Reena Kumari Chopra

Clemens Meyer

A method is disclosed of processing a set of images. Each image in the set has an associated counterpart image. One or more regions of interest (ROIs) are identified in one or more of the images in the set of images. For ROI identified, a reference region is identified in the associated counterpart image. ROIs and associated reference regions are cropped out, thereby forming cropped pairs of images 1 . . . n, that are fed to a deep learning model trained to make a prediction of probability of a state of the ROI, e.g., disease state, which generates a prediction P-, (i=1 . . . n) for each cropped pair. The model generates an overall prediction P from each of the predictions P. A visualization of the set of medical images and the associated counterpart images including the cropped pair of images is generated.

System and method for interpretation of multiple medical images using deep learning

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

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for segmenting a medical image. In one aspect, a method comprises: receiving a medical image that is captured using a medical imaging modality and that depicts a region of tissue in a body; and processing the medical image using a segmentation neural network to generate a segmentation output. The segmentation neural network can include a sequence of multiple encoder blocks and a decoder subnetwork. Training the segmentation neural network can include determining a set of error values for a segmentation channel; identifying the highest error values from the set of error values for the segmentation channel; and determining a segmentation loss based on the highest error values identified for the segmentation channel.

3-D convolutional neural networks for organ segmentation in medical images for radiotherapy planning

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for segmenting a medical image. In one aspect, a method comprises: receiving a medical image that is captured using a medical imaging modality and that depicts a region of tissue in a body; and processing the medical image using a segmentation neural network to generate a segmentation output, wherein the segmentation neural network comprises a sequence of multiple encoder blocks, wherein: each encoder block is a residual neural network block comprising one or more two-dimensional convolutional neural network layers, one or more three-dimensional convolutional neural network layers, or both, and each encoder block is configured to process a respective encoder block input to generate a respective encoder block output wherein a spatial resolution of the encoder block output is lower than a spatial resolution of the encoder block input.

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London, United Kingdom

Jeffrey De Fauw