Research Blog

Quick Access in Drive: Using Machine Learning to Save You Time

Friday, March 10, 2017

Posted by Sandeep Tata, Software Engineer, Google Researchlanguage translationsunderstanding imageshelping you respond to emailsstudiesQuick AccessWebAndroidiOS

Quick Access suggestions on the top row in Drive on a desktop browser.

this talk from Google Cloud Next ‘17Acknowledgements

Assisting Pathologists in Detecting Cancer with Deep Learning

Friday, March 03, 2017

Posted by Martin Stumpe, Technical Lead, and Lily Peng, Product Manageras low as 48%similarly lowRadboud University Medical Center2016 ISBI Camelyon Challenge¹Inception (aka GoogLeNet) worked reasonably well

Left: Images from two lymph node biopsies. Middle: earlier results of our deep learning tumor detection. Right: our current results. Notice the visibly reduced noise (potential false positives) between the two versions.

FROC²scores as high as 81%Detecting Cancer Metastases on Gigapixel Pathology Images

A closeup of a lymph node biopsy. The tissue contains a breast cancer metastasis as well as macrophages, which look similar to tumor but are benign normal tissue. Our algorithm successfully identifies the tumor region (bright green) and is not confused by the macrophages.

Like most metrics, the FROC localization score is not perfect. Here, the FROC score is defined as the sensitivity (percentage of tumors detected) at a few pre-defined average false positives per slide. It is pretty rare for a pathologist to make a false positive call (mistaking normal cells as tumor). For example, the score of 73% mentioned above corresponds to a 73% sensitivity and zero false positives. By contrast, our algorithm’s sensitivity rises when more false positives are allowed. At 8 false positives per slide, our algorithms had a sensitivity of 92%.

These algorithms perform well for the tasks for which they are trained, but lack the breadth of knowledge and experience of human pathologists — for example, being able to detect other abnormalities that the model has not been explicitly trained to classify (e.g. inflammatory process, autoimmune disease, or other types of cancer).

To ensure the best clinical outcome for patients, these algorithms need to be incorporated in a way that complements the pathologist’s workflow. We envision that algorithm such as ours could improve the efficiency and consistency of pathologists. For example, pathologists could reduce their false negative rates (percentage of undetected tumors) by reviewing the top ranked predicted tumor regions including up to 8 false positive regions per slide. As another example, these algorithms could enable pathologists to easily and accurately measure tumor size, a factor that is associated with prognosis.

1 For those who might be interested, the Camelyon17 challenge, which builds upon the 2016 challenge, is currently underway.^↩

2 The pathologist ended up spending 30 hours on this task on 130 slides.^↩

Google Research Awards 2016

Thursday, February 23, 2017

Posted by Maggie Johnson, Director of Education and University Relations, GoogleGoogle Research Awardsmachine learningmachine perceptionnatural language processingsecurity

The subject areas that received the most support were machine learning, machine perception, networking and systems.

Proposals related to Machine learning represented 20% of the total submissions received, up from 12% in 2015.

Proportionally, proposals from Europe had a 4% higher acceptance rate, attributed to our increased research presence in Zürich.

recipients of this round’s awardsour website

Preprocessing for Machine Learning with tf.Transform

Wednesday, February 22, 2017

Posted by Kester Tong, David Soergel, and Gus Katsiapis, Software Engineerstf.TransformApache BeamGoogle Cloud Dataflowother frameworksTensorflow Serving

tf.Transform allows users to define a preprocessing pipeline. Users can materialize the preprocessed data for use in TensorFlow training, and also export a tf.Transform graph that encodes the transformations as a TensorFlow graph. This transformation graph can then be incorporated into the model graph used for inference.

AcknowledgementsTensorFlowTensorFlow ServingCloud Dataflow

Headset “Removal” for Virtual and Mixed Reality

Tuesday, February 21, 2017

Posted by Vivek Kwatra, Research Scientist and Christian Frueh, Avneesh Sud, Software Engineers
new ways to view the worldrealimaginaryMixed RealityMachine PerceptionDaydream LabsYouTube Spaces

VR user captured in front of a green-screen is blended with the virtual environment to generate the MR output: Traditional MR output has the user face occluded, while our result reveals the face. Note how the headset is modified with a marker to aid tracking.

enhancing Mixed RealityGoogle-VR blogDynamic face model capturegaze-dependent dynamic appearancegaze database

On the left, the user’s face is captured by a camera as she tracks a marker on the monitor with her eyes. On the right, we show the dynamic nature of reconstructed 3D face model: by moving or clicking on the mouse, we are able to simulate both apparent eye gaze and blinking.

Calibration and AlignmentCreating a Mixed Reality videoCompositing and Renderingincorporate eye-trackinguncanny valleyResults and Extensionsheadset removal technology to enhance Mixed RealityGoogle Tilt Brush

An artist creates 3D art using Google Tilt Brush, shown in Mixed Reality. On the top is the traditional MR result where the face is hidden behind the headset. On the bottom is our result, which reveals the entire face and eyes for a more natural and engaging experience.

The CS Capacity Program - New Tools and SIGCSE 2017

Thursday, February 16, 2017

Posted by Chris Stephenson, Head of Computer Science Education StrategyCS Capacity programdramatic increase in undergraduate computer science enrollmentsSPARCMaGE Peer Mentor programentire online course curriculum

MaGE Program Students and Faculty from Mount Holyoke College

a small-group tutoring programSIGCSE 2017 Technical SymposiumAutolabMy Digital HandSIGCSE conference in March

An updated YouTube-8M, a video understanding challenge, and a CVPR workshop. Oh my!

Wednesday, February 15, 2017

Posted by Paul Natsev, Software EngineerYouTube-8M datasetmillions of videos labeled with thousands of classesOpen ImagesYouTube-BoundingBoxesYouTube-8M datasetGoogle Cloud Machine Learningkaggle.comvideo understanding competitionCVPR’17 WorkshopAn Updated YouTube-8Maudio modeling architecturehere

A tree-map visualization of the updated YouTube-8M dataset, organized into 24 high-level verticals, including the top-200 most frequent entities, plus the top-5 entities for each vertical.

Sample videos from the top-18 high-level verticals in the YouTube-8M dataset.

The Google Cloud & YouTube-8M Video Understanding Challenge Google Cloud & YouTube-8M Video Understanding ChallengeGoogle Cloudkaggle.comKaggle competitionhereGoogle Cloud Machine LearningGithubREADMEgetting started guide on KaggleThe CVPR 2017 Workshop on YouTube-8M Large-Scale Video Understanding 1st YouTube-8M WorkshopCVPR 2017inviteAcknowledgements

Google Research Blog

Quick Access in Drive: Using Machine Learning to Save You Time

Assisting Pathologists in Detecting Cancer with Deep Learning

Google Research Awards 2016

Preprocessing for Machine Learning with tf.Transform

Headset “Removal” for Virtual and Mixed Reality

The CS Capacity Program - New Tools and SIGCSE 2017

An updated YouTube-8M, a video understanding challenge, and a CVPR workshop. Oh my!

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