Google Inc.

Natasha Antropova

Jeffrey De Fauw

Shravya Shetty

Scott McKinney

Marcin Sieniek

Varun Godbole

Jonathan Godwin

Christopher Kelly

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

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London, United Kingdom

Natasha Antropova