Blog
The latest news from Google AI
An Augmented Reality Microscope for Cancer Detection
Monday, April 16, 2018
Posted by Martin Stumpe, Technical Lead and Craig Mermel, Product Manager, Google Brain Team
(Updated Aug 12, 2019: The work described in this blogpost has been
published
in
Nature Medicine
.)
Applications of
deep learning
to medical disciplines including
ophthalmology
,
dermatology
,
radiology
, and
pathology
have recently shown great promise to increase both the accuracy and availability of high-quality healthcare to patients around the world. At Google, we have also
published results
showing that a
convolutional neural network
is able to detect breast cancer metastases in lymph nodes at a level of accuracy comparable to a trained pathologist. However, because direct tissue visualization using a compound light microscope remains the predominant means by which a pathologist diagnoses illness, a critical barrier to the widespread adoption of deep learning in pathology is the dependence on having a digital representation of the microscopic tissue.
Today, in a
talk
delivered at the Annual Meeting of the American Association for Cancer Research (
AACR
), with an accompanying paper “
An Augmented Reality Microscope for Real-time Automated Detection of Cancer
”, we describe a prototype Augmented Reality Microscope (ARM) platform that we believe can possibly help accelerate and democratize the adoption of deep learning tools for pathologists around the world. The platform consists of a modified light microscope that enables real-time image analysis and presentation of the results of machine learning algorithms directly into the field of view. Importantly, the ARM can be retrofitted into existing light microscopes found in hospitals and clinics around the world using low-cost, readily-available components, and without the need for whole slide digital versions of the tissue being analyzed.
Modern computational components and deep learning models, such as those built upon
TensorFlow
, will allow a wide range of pre-trained models to run on this platform. As in a traditional analog microscope, the user views the sample through the eyepiece. A machine learning algorithm projects its output back into the optical path in real-time. This digital projection is visually superimposed on the original (analog) image of the specimen to assist the viewer in localizing or quantifying features of interest. Importantly, the computation and visual feedback updates quickly — our present implementation runs at approximately 10 frames per second, so the model output updates seamlessly as the user scans the tissue by moving the slide and/or changing magnification.
Left: Schematic overview of the ARM. A digital camera captures the same field of view (FoV) as the user and passes the image to an attached compute unit capable of running real-time inference of a machine learning model. The results are fed back into a custom AR display which is inline with the ocular lens and projects the model output on the same plane as the slide. Right: A picture of our prototype which has been retrofitted into a typical clinical-grade light microscope.
In principle, the ARM can provide a wide variety of visual feedback, including text, arrows, contours, heatmaps, or animations, and is capable of running many types of machine learning algorithms aimed at solving different problems such as object detection, quantification, or classification.
As a demonstration of the potential utility of the ARM, we configured it to run two different cancer detection algorithms: one that detects breast cancer metastases in lymph node specimens, and another that detects prostate cancer in prostatectomy specimens. These models can run at magnifications between 4-40x, and the result of a given model is displayed by outlining detected tumor regions with a green contour. These contours help draw the pathologist’s attention to areas of interest without obscuring the underlying tumor cell appearance.
Example view through the lens of the ARM. These images show examples of the lymph node metastasis model with 4x, 10x, 20x, and 40x microscope objectives.
While both cancer models were originally trained on images from a whole slide scanner with a significantly different optical configuration, the models performed remarkably well on the ARM with no additional re-training. For example, the lymph node metastasis model had an area-under-the-curve (AUC) of 0.98 and our prostate cancer model had an AUC of 0.96 for cancer detection in the field of view (FoV) when run on the ARM, only slightly decreased performance than obtained on WSI. We believe it is likely that the performance of these models can be further improved by additional training on digital images captured directly from the ARM itself.
We believe that the ARM has potential for a large impact on global health, particularly for the diagnosis of infectious diseases, including tuberculosis and malaria, in developing countries. Furthermore, even in hospitals that will adopt a digital pathology workflow in the near future, ARM could be used in combination with the digital workflow where scanners still face major challenges or where rapid turnaround is required (e.g. cytology, fluorescent imaging, or intra-operative frozen sections). Of course, light microscopes have proven useful in many industries other than pathology, and we believe the ARM can be adapted for a broad range of applications across healthcare, life sciences research, and material science. We’re excited to continue to explore how the ARM can help accelerate the adoption of machine learning for positive impact around the world.
Labels
2018
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
