Google Inc.

University of California

Jitendra Malik

Sergey Levine

Rahul Sukthankar

Christian Haene

Saurabh Gupta

James Christopher Davidson

Pratik Mukherjee

Esther L Yuh

Wei-cheng Kuo

Sergey Vladimir Levine

A computer-implemented method can include a training phase and a hemorrhage detection phase. The training phase can include: receiving a first plurality of frames from at least one original computed tomography (CT) scan of a target subject, wherein each frame may or may not include a visual indication of a hemorrhage, and further wherein each frame including a visual indication of a hemorrhage has at least one label associated therewith; and using a fully convolutional neural network (FCN) to train a model by determining, for each of the first plurality of frames, whether at least one sub-portion of the frame includes a visual indication of a hemorrhage and classifying the sub-portion of the frame based on the determining. The hemorrhage detection phase can include: receiving a second plurality of frames from a CT scan of a target subject, wherein each frame may or may not include a visual indication of a hemorrhage; and determining, for each of the second plurality of frames, whether a plurality of sub-portions of the frame includes a visual indication of a hemorrhage based at least in part on the trained model.

Expert-level detection of acute intracranial hemorrhage on head CT scans

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for navigation using visual inputs. One of the systems includes a mapping subsystem configured to, at each time step of a plurality of time steps, generate a characterization of an environment from an image of the environment at the time step, wherein the characterization comprises an environment map identifying locations in the environment having a particular characteristic, and wherein generating the characterization comprises, for each time step: obtaining the image of the environment at the time step, processing the image to generate a first initial characterization for the time step, obtaining a final characterization for a previous time step, processing the characterization for the previous time step to generate a second initial characterization for the time step, and combining the first initial characterization and the second initial characterization to generate a final characterization for the time step.

Agent navigation using visual inputs

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Berkeley, CA, United States of America

Jitendra Malik