Tag Archives: Health

An Augmented Reality Microscope for Cancer Detection



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” (under review), 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.


Source: Google AI Blog


Making Healthcare Data Work Better with Machine Learning



Over the past 10 years, healthcare data has moved from being largely on paper to being almost completely digitized in electronic health records. But making sense of this data involves a few key challenges. First, there is no common data representation across vendors; each uses a different way to structure their data. Second, even sites that use the same vendor may differ significantly, for example, they typically use different codes for the same medication. Third, data can be spread over many tables, some containing encounters, some containing lab results, and yet others containing vital signs.

The Fast Healthcare Interoperability Resources (FHIR) standard addresses most of these challenges: it has a solid yet extensible data-model, is built on established Web standards, and is rapidly becoming the de-facto standard for both individual records and bulk-data access. But to enable large-scale machine learning, we needed a few additions: implementations in various programming languages, an efficient way to serialize large amounts of data to disk, and a representation that allows analyses of large datasets.

Today, we are happy to open source a protocol buffer implementation of the FHIR standard, which addresses these issues. The current version supports Java, and support for C++, Go, and Python will follow soon. Support for profiles will follow shortly as well, plus tools to help convert legacy data into FHIR.

FHIR as the core data model
Over the past few years, as we’ve been partnering with academic medical centers to apply machine learning to de-identified medical records, it became clear that we needed to address the complexity of healthcare data head-on. Indeed, for machine learning to be effective on medical data, we need a holistic view of what happened to each patient over time. And as a bonus, we want a data representation that is directly applicable in a clinical setting.

While the FHIR standard addresses most of our needs, making healthcare data substantially easier to manage than “legacy” data structures and enabling large-scale machine-learning independent of vendors, we believe the introduction of protocol buffers can help both application developers and (machine-learning) researchers use FHIR.

Current release of protocol buffers
We’ve taken care to make our protocol buffer representation suitable for both programmatic access and database queries. One of the provided examples shows how to upload FHIR data into Google Cloud BigQuery and have it available for querying, and we are adding other examples that upload directly from bulk data export. Our protocol buffers adhere to the FHIR standard (they are in fact auto-generated from it) but make for more elegant queries.

The current release does not yet include support for training TensorFlow models, but keep an eye out for future updates. We aim to open-source as much as possible of our recent work, to help make our research more reproducible and applicable to real-world scenarios. Furthermore, we are working closely with our colleagues in Google Cloud on more tools for managing healthcare data at scale.

Acknowledgements
We enjoyed great discussions and helpful feedback from the FHIR community, including Grahame Grieve, Ewout Kramer, Josh Mandel and others. Thanks to our colleagues at DeepMind, the Google Brain team and our academic collaborators.

Making Healthcare Data Work Better with Machine Learning



Over the past 10 years, healthcare data has moved from being largely on paper to being almost completely digitized in electronic health records. But making sense of this data involves a few key challenges. First, there is no common data representation across vendors; each uses a different way to structure their data. Second, even sites that use the same vendor may differ significantly, for example, they typically use different codes for the same medication. Third, data can be spread over many tables, some containing encounters, some containing lab results, and yet others containing vital signs.

The Fast Healthcare Interoperability Resources (FHIR) standard addresses most of these challenges: it has a solid yet extensible data-model, is built on established Web standards, and is rapidly becoming the de-facto standard for both individual records and bulk-data access. But to enable large-scale machine learning, we needed a few additions: implementations in various programming languages, an efficient way to serialize large amounts of data to disk, and a representation that allows analyses of large datasets.

Today, we are happy to open source a protocol buffer implementation of the FHIR standard, which addresses these issues. The current version supports Java, and support for C++, Go, and Python will follow soon. Support for profiles will follow shortly as well, plus tools to help convert legacy data into FHIR.

FHIR as the core data model
Over the past few years, as we’ve been partnering with academic medical centers to apply machine learning to de-identified medical records, it became clear that we needed to address the complexity of healthcare data head-on. Indeed, for machine learning to be effective on medical data, we need a holistic view of what happened to each patient over time. And as a bonus, we want a data representation that is directly applicable in a clinical setting.

While the FHIR standard addresses most of our needs, making healthcare data substantially easier to manage than “legacy” data structures and enabling large-scale machine-learning independent of vendors, we believe the introduction of protocol buffers can help both application developers and (machine-learning) researchers use FHIR.

Current release of protocol buffers
We’ve taken care to make our protocol buffer representation suitable for both programmatic access and database queries. One of the provided examples shows how to upload FHIR data into Google Cloud BigQuery and have it available for querying, and we are adding other examples that upload directly from bulk data export. Our protocol buffers adhere to the FHIR standard (they are in fact auto-generated from it) but make for more elegant queries.

The current release does not yet include support for training TensorFlow models, but keep an eye out for future updates. We aim to open-source as much as possible of our recent work, to help make our research more reproducible and applicable to real-world scenarios. Furthermore, we are working closely with our colleagues in Google Cloud on more tools for managing healthcare data at scale.

Acknowledgements
We enjoyed great discussions and helpful feedback from the FHIR community, including Grahame Grieve, Ewout Kramer, Josh Mandel and others. Thanks to our colleagues at DeepMind, the Google Brain team and our academic collaborators.

Assessing Cardiovascular Risk Factors with Computer Vision



Heart attacks, strokes and other cardiovascular (CV) diseases continue to be among the top public health issues. Assessing this risk is critical first step toward reducing the likelihood that a patient suffers a CV event in the future. To do this assessment, doctors take into account a variety of risk factors — some genetic (like age and sex), some with lifestyle components (like smoking and blood pressure). While most of these factors can be obtained by simply asking the patient, others factors, like cholesterol, require a blood draw. Doctors also take into account whether or not a patient has another disease, such as diabetes, which is associated with significantly increased risk of CV events.

Recently, we’ve seen many examples [1–4] of how deep learning techniques can help to increase the accuracy of diagnoses for medical imaging, especially for diabetic eye disease. In “Prediction of Cardiovascular Risk Factors from Retinal Fundus Photographs via Deep Learning,” published in Nature Biomedical Engineering, we show that in addition to detecting eye disease, images of the eye can very accurately predict other indicators of CV health. This discovery is particularly exciting because it suggests we might discover even more ways to diagnose health issues from retinal images.

Using deep learning algorithms trained on data from 284,335 patients, we were able to predict CV risk factors from retinal images with surprisingly high accuracy for patients from two independent datasets of 12,026 and 999 patients. For example, our algorithm could distinguish the retinal images of a smoker from that of a non-smoker 71% of the time. In addition, while doctors can typically distinguish between the retinal images of patients with severe high blood pressure and normal patients, our algorithm could go further to predict the systolic blood pressure within 11 mmHg on average for patients overall, including those with and without high blood pressure.
LEFT: image of the back of the eye showing the macula (dark spot in the middle), optic disc (bright spot at the right), and blood vessels (dark red lines arcing out from the bright spot on the right). RIGHT: retinal image in gray, with the pixels used by the deep learning algorithm to make predictions about the blood pressure highlighted in shades of green (heatmap). We found that each CV risk factor prediction uses a distinct pattern, such as blood vessels for blood pressure, and optic disc for other predictions.
In addition to predicting the various risk factors (age, gender, smoking, blood pressure, etc) from retinal images, our algorithm was fairly accurate at predicting the risk of a CV event directly. Our algorithm used the entire image to quantify the association between the image and the risk of heart attack or stroke. Given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70% of the time. This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol.

More importantly, we opened the “black box” by using attention techniques to look at how the algorithm was making its prediction. These techniques allow us to generate a heatmap that shows which pixels were the most important for a predicting a specific CV risk factor. For example, the algorithm paid more attention to blood vessels for making predictions about blood pressure, as shown in the image above. Explaining how the algorithm is making its prediction gives doctor more confidence in the algorithm itself. In addition, this technique could help generate hypotheses for future scientific investigations into CV risk and the retina.

At the broadest level, we are excited about this work because it may represent a new method of scientific discovery. Traditionally, medical discoveries are often made through a sophisticated form of guess and test — making hypotheses from observations and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can be difficult because of the wide variety of features, patterns, colors, values and shapes that are present in real images. Our approach uses deep learning to draw connections between changes in the human anatomy and disease, akin to how doctors learn to associate signs and symptoms with the diagnosis of a new disease. This could help scientists generate more targeted hypotheses and drive a wide range of future research.

With these promising results, a lot of scientific work remains. Our dataset had many images labeled with smoking status, systolic blood pressure, age, gender and other variables, but it only had a few hundred examples of CV events. We look forward to developing and testing our algorithm on larger and more comprehensive datasets. To make this useful for patients, we will be seeking to understand the effects of interventions such as lifestyle changes or medications on our risk predictions and we will be generating new hypotheses and theories to test.


References

[1] Gulshan, V. et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 316, 2402–2410 (2016).

[2] Ting, D. S. W. et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA 318, 2211–2223 (2017).

[3] Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature (2017). doi:10.1038/nature21056

[4] Ehteshami Bejnordi, B. et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA 318, 2199–2210 (2017).

DeepVariant: Highly Accurate Genomes With Deep Neural Networks

Crossposted on the Google Research Blog

Across many scientific disciplines, but in particular in the field of genomics, major breakthroughs have often resulted from new technologies. From Sanger sequencing, which made it possible to sequence the human genome, to the microarray technologies that enabled the first large-scale genome-wide experiments, new instruments and tools have allowed us to look ever more deeply into the genome and apply the results broadly to health, agriculture and ecology.

One of the most transformative new technologies in genomics was high-throughput sequencing (HTS), which first became commercially available in the early 2000s. HTS allowed scientists and clinicians to produce sequencing data quickly, cheaply, and at scale. However, the output of HTS instruments is not the genome sequence for the individual being analyzed — for humans this is 3 billion paired bases (guanine, cytosine, adenine and thymine) organized into 23 pairs of chromosomes. Instead, these instruments generate ~1 billion short sequences, known as reads. Each read represents just 100 of the 3 billion bases, and per-base error rates range from 0.1-10%. Processing the HTS output into a single, accurate and complete genome sequence is a major outstanding challenge. The importance of this problem, for biomedical applications in particular, has motivated efforts such as the Genome in a Bottle Consortium (GIAB), which produces high confidence human reference genomes that can be used for validation and benchmarking, as well as the precisionFDA community challenges, which are designed to foster innovation that will improve the quality and accuracy of HTS-based genomic tests.

CAPTION: For any given location in the genome, there are multiple reads among the ~1 billion that include a base at that position. Each read is aligned to a reference, and then each of the bases in the read is compared to the base of the reference at that location. When a read includes a base that differs from the reference, it may indicate a variant (a difference in the true sequence), or it may be an error.

Today, we announce the open source release of DeepVariant, a deep learning technology to reconstruct the true genome sequence from HTS sequencer data with significantly greater accuracy than previous classical methods. This work is the product of more than two years of research by the Google Brain team, in collaboration with Verily Life Sciences. DeepVariant transforms the task of variant calling, as this reconstruction problem is known in genomics, into an image classification problem well-suited to Google's existing technology and expertise.

CAPTION: Each of the four images above is a visualization of actual sequencer reads aligned to a reference genome. A key question is how to use the reads to determine whether there is a variant on both chromosomes, on just one chromosome, or on neither chromosome. There is more than one type of variant, with SNPs and insertions/deletions being the most common. A: a true SNP on one chromosome pair, B: a deletion on one chromosome, C: a deletion on both chromosomes, D: a false variant caused by errors. It's easy to see that these look quite distinct when visualized in this manner.

We started with GIAB reference genomes, for which there is high-quality ground truth (or the closest approximation currently possible). Using multiple replicates of these genomes, we produced tens of millions of training examples in the form of multi-channel tensors encoding the HTS instrument data, and then trained a TensorFlow-based image classification model to identify the true genome sequence from the experimental data produced by the instruments. Although the resulting deep learning model, DeepVariant, had no specialized knowledge about genomics or HTS, within a year it had won the the highest SNP accuracy award at the precisionFDA Truth Challenge, outperforming state-of-the-art methods. Since then, we've further reduced the error rate by more than 50%.


DeepVariant is being released as open source software to encourage collaboration and to accelerate the use of this technology to solve real world problems. To further this goal, we partnered with Google Cloud Platform (GCP) to deploy DeepVariant workflows on GCP, available today, in configurations optimized for low-cost and fast turnarounds using scalable GCP technologies like the Pipelines API. This paired set of releases provides a smooth ramp for users to explore and evaluate the capabilities of DeepVariant in their current compute environment while providing a scalable, cloud-based solution to satisfy the needs of even the largest genomics datasets.

DeepVariant is the first of what we hope will be many contributions that leverage Google's computing infrastructure and ML expertise to both better understand the genome and to provide deep learning-based genomics tools to the community. This is all part of a broader goal to apply Google technologies to healthcare and other scientific applications, and to make the results of these efforts broadly accessible.

By Mark DePristo and Ryan Poplin, Google Brain Team

DeepVariant: Highly Accurate Genomes With Deep Neural Networks

Crossposted on the Google Research Blog

Across many scientific disciplines, but in particular in the field of genomics, major breakthroughs have often resulted from new technologies. From Sanger sequencing, which made it possible to sequence the human genome, to the microarray technologies that enabled the first large-scale genome-wide experiments, new instruments and tools have allowed us to look ever more deeply into the genome and apply the results broadly to health, agriculture and ecology.

One of the most transformative new technologies in genomics was high-throughput sequencing (HTS), which first became commercially available in the early 2000s. HTS allowed scientists and clinicians to produce sequencing data quickly, cheaply, and at scale. However, the output of HTS instruments is not the genome sequence for the individual being analyzed — for humans this is 3 billion paired bases (guanine, cytosine, adenine and thymine) organized into 23 pairs of chromosomes. Instead, these instruments generate ~1 billion short sequences, known as reads. Each read represents just 100 of the 3 billion bases, and per-base error rates range from 0.1-10%. Processing the HTS output into a single, accurate and complete genome sequence is a major outstanding challenge. The importance of this problem, for biomedical applications in particular, has motivated efforts such as the Genome in a Bottle Consortium (GIAB), which produces high confidence human reference genomes that can be used for validation and benchmarking, as well as the precisionFDA community challenges, which are designed to foster innovation that will improve the quality and accuracy of HTS-based genomic tests.

CAPTION: For any given location in the genome, there are multiple reads among the ~1 billion that include a base at that position. Each read is aligned to a reference, and then each of the bases in the read is compared to the base of the reference at that location. When a read includes a base that differs from the reference, it may indicate a variant (a difference in the true sequence), or it may be an error.

Today, we announce the open source release of DeepVariant, a deep learning technology to reconstruct the true genome sequence from HTS sequencer data with significantly greater accuracy than previous classical methods. This work is the product of more than two years of research by the Google Brain team, in collaboration with Verily Life Sciences. DeepVariant transforms the task of variant calling, as this reconstruction problem is known in genomics, into an image classification problem well-suited to Google's existing technology and expertise.

CAPTION: Each of the four images above is a visualization of actual sequencer reads aligned to a reference genome. A key question is how to use the reads to determine whether there is a variant on both chromosomes, on just one chromosome, or on neither chromosome. There is more than one type of variant, with SNPs and insertions/deletions being the most common. A: a true SNP on one chromosome pair, B: a deletion on one chromosome, C: a deletion on both chromosomes, D: a false variant caused by errors. It's easy to see that these look quite distinct when visualized in this manner.

We started with GIAB reference genomes, for which there is high-quality ground truth (or the closest approximation currently possible). Using multiple replicates of these genomes, we produced tens of millions of training examples in the form of multi-channel tensors encoding the HTS instrument data, and then trained a TensorFlow-based image classification model to identify the true genome sequence from the experimental data produced by the instruments. Although the resulting deep learning model, DeepVariant, had no specialized knowledge about genomics or HTS, within a year it had won the the highest SNP accuracy award at the precisionFDA Truth Challenge, outperforming state-of-the-art methods. Since then, we've further reduced the error rate by more than 50%.


DeepVariant is being released as open source software to encourage collaboration and to accelerate the use of this technology to solve real world problems. To further this goal, we partnered with Google Cloud Platform (GCP) to deploy DeepVariant workflows on GCP, available today, in configurations optimized for low-cost and fast turnarounds using scalable GCP technologies like the Pipelines API. This paired set of releases provides a smooth ramp for users to explore and evaluate the capabilities of DeepVariant in their current compute environment while providing a scalable, cloud-based solution to satisfy the needs of even the largest genomics datasets.

DeepVariant is the first of what we hope will be many contributions that leverage Google's computing infrastructure and ML expertise to both better understand the genome and to provide deep learning-based genomics tools to the community. This is all part of a broader goal to apply Google technologies to healthcare and other scientific applications, and to make the results of these efforts broadly accessible.

By Mark DePristo and Ryan Poplin, Google Brain Team

DeepVariant: Highly Accurate Genomes With Deep Neural Networks

Crossposted on the Google Research Blog

Across many scientific disciplines, but in particular in the field of genomics, major breakthroughs have often resulted from new technologies. From Sanger sequencing, which made it possible to sequence the human genome, to the microarray technologies that enabled the first large-scale genome-wide experiments, new instruments and tools have allowed us to look ever more deeply into the genome and apply the results broadly to health, agriculture and ecology.

One of the most transformative new technologies in genomics was high-throughput sequencing (HTS), which first became commercially available in the early 2000s. HTS allowed scientists and clinicians to produce sequencing data quickly, cheaply, and at scale. However, the output of HTS instruments is not the genome sequence for the individual being analyzed — for humans this is 3 billion paired bases (guanine, cytosine, adenine and thymine) organized into 23 pairs of chromosomes. Instead, these instruments generate ~1 billion short sequences, known as reads. Each read represents just 100 of the 3 billion bases, and per-base error rates range from 0.1-10%. Processing the HTS output into a single, accurate and complete genome sequence is a major outstanding challenge. The importance of this problem, for biomedical applications in particular, has motivated efforts such as the Genome in a Bottle Consortium (GIAB), which produces high confidence human reference genomes that can be used for validation and benchmarking, as well as the precisionFDA community challenges, which are designed to foster innovation that will improve the quality and accuracy of HTS-based genomic tests.

CAPTION: For any given location in the genome, there are multiple reads among the ~1 billion that include a base at that position. Each read is aligned to a reference, and then each of the bases in the read is compared to the base of the reference at that location. When a read includes a base that differs from the reference, it may indicate a variant (a difference in the true sequence), or it may be an error.

Today, we announce the open source release of DeepVariant, a deep learning technology to reconstruct the true genome sequence from HTS sequencer data with significantly greater accuracy than previous classical methods. This work is the product of more than two years of research by the Google Brain team, in collaboration with Verily Life Sciences. DeepVariant transforms the task of variant calling, as this reconstruction problem is known in genomics, into an image classification problem well-suited to Google's existing technology and expertise.

CAPTION: Each of the four images above is a visualization of actual sequencer reads aligned to a reference genome. A key question is how to use the reads to determine whether there is a variant on both chromosomes, on just one chromosome, or on neither chromosome. There is more than one type of variant, with SNPs and insertions/deletions being the most common. A: a true SNP on one chromosome pair, B: a deletion on one chromosome, C: a deletion on both chromosomes, D: a false variant caused by errors. It's easy to see that these look quite distinct when visualized in this manner.

We started with GIAB reference genomes, for which there is high-quality ground truth (or the closest approximation currently possible). Using multiple replicates of these genomes, we produced tens of millions of training examples in the form of multi-channel tensors encoding the HTS instrument data, and then trained a TensorFlow-based image classification model to identify the true genome sequence from the experimental data produced by the instruments. Although the resulting deep learning model, DeepVariant, had no specialized knowledge about genomics or HTS, within a year it had won the the highest SNP accuracy award at the precisionFDA Truth Challenge, outperforming state-of-the-art methods. Since then, we've further reduced the error rate by more than 50%.


DeepVariant is being released as open source software to encourage collaboration and to accelerate the use of this technology to solve real world problems. To further this goal, we partnered with Google Cloud Platform (GCP) to deploy DeepVariant workflows on GCP, available today, in configurations optimized for low-cost and fast turnarounds using scalable GCP technologies like the Pipelines API. This paired set of releases provides a smooth ramp for users to explore and evaluate the capabilities of DeepVariant in their current compute environment while providing a scalable, cloud-based solution to satisfy the needs of even the largest genomics datasets.

DeepVariant is the first of what we hope will be many contributions that leverage Google's computing infrastructure and ML expertise to both better understand the genome and to provide deep learning-based genomics tools to the community. This is all part of a broader goal to apply Google technologies to healthcare and other scientific applications, and to make the results of these efforts broadly accessible.

By Mark DePristo and Ryan Poplin, Google Brain Team

DeepVariant: Highly Accurate Genomes With Deep Neural Networks



(Crossposted on the Google Open Source Blog)

Across many scientific disciplines, but in particular in the field of genomics, major breakthroughs have often resulted from new technologies. From Sanger sequencing, which made it possible to sequence the human genome, to the microarray technologies that enabled the first large-scale genome-wide experiments, new instruments and tools have allowed us to look ever more deeply into the genome and apply the results broadly to health, agriculture and ecology.

One of the most transformative new technologies in genomics was high-throughput sequencing (HTS), which first became commercially available in the early 2000s. HTS allowed scientists and clinicians to produce sequencing data quickly, cheaply, and at scale. However, the output of HTS instruments is not the genome sequence for the individual being analyzed — for humans this is 3 billion paired bases (guanine, cytosine, adenine and thymine) organized into 23 pairs of chromosomes. Instead, these instruments generate ~1 billion short sequences, known as reads. Each read represents just 100 of the 3 billion bases, and per-base error rates range from 0.1-10%. Processing the HTS output into a single, accurate and complete genome sequence is a major outstanding challenge. The importance of this problem, for biomedical applications in particular, has motivated efforts such as the Genome in a Bottle Consortium (GIAB), which produces high confidence human reference genomes that can be used for validation and benchmarking, as well as the precisionFDA community challenges, which are designed to foster innovation that will improve the quality and accuracy of HTS-based genomic tests.
For any given location in the genome, there are multiple reads among the ~1 billion that include a base at that position. Each read is aligned to a reference, and then each of the bases in the read is compared to the base of the reference at that location. When a read includes a base that differs from the reference, it may indicate a variant (a difference in the true sequence), or it may be an error.
Today, we announce the open source release of DeepVariant, a deep learning technology to reconstruct the true genome sequence from HTS sequencer data with significantly greater accuracy than previous classical methods. This work is the product of more than two years of research by the Google Brain team, in collaboration with Verily Life Sciences. DeepVariant transforms the task of variant calling, as this reconstruction problem is known in genomics, into an image classification problem well-suited to Google's existing technology and expertise.
Each of the four images above is a visualization of actual sequencer reads aligned to a reference genome. A key question is how to use the reads to determine whether there is a variant on both chromosomes, on just one chromosome, or on neither chromosome. There is more than one type of variant, with SNPs and insertions/deletions being the most common. A: a true SNP on one chromosome pair, B: a deletion on one chromosome, C: a deletion on both chromosomes, D: a false variant caused by errors. It's easy to see that these look quite distinct when visualized in this manner.
We started with GIAB reference genomes, for which there is high-quality ground truth (or the closest approximation currently possible). Using multiple replicates of these genomes, we produced tens of millions of training examples in the form of multi-channel tensors encoding the HTS instrument data, and then trained a TensorFlow-based image classification model to identify the true genome sequence from the experimental data produced by the instruments. Although the resulting deep learning model, DeepVariant, had no specialized knowledge about genomics or HTS, within a year it had won the the highest SNP accuracy award at the precisionFDA Truth Challenge, outperforming state-of-the-art methods. Since then, we've further reduced the error rate by more than 50%.
DeepVariant is being released as open source software to encourage collaboration and to accelerate the use of this technology to solve real world problems. To further this goal, we partnered with Google Cloud Platform (GCP) to deploy DeepVariant workflows on GCP, available today, in configurations optimized for low-cost and fast turnarounds using scalable GCP technologies like the Pipelines API. This paired set of releases provides a smooth ramp for users to explore and evaluate the capabilities of DeepVariant in their current compute environment while providing a scalable, cloud-based solution to satisfy the needs of even the largest genomics datasets.

DeepVariant is the first of what we hope will be many contributions that leverage Google's computing infrastructure and ML expertise to both better understand the genome and to provide deep learning-based genomics tools to the community. This is all part of a broader goal to apply Google technologies to healthcare and other scientific applications, and to make the results of these efforts broadly accessible.

Assisting Pathologists in Detecting Cancer with Deep Learning



A pathologist’s report after reviewing a patient’s biological tissue samples is often the gold standard in the diagnosis of many diseases. For cancer in particular, a pathologist’s diagnosis has a profound impact on a patient’s therapy. The reviewing of pathology slides is a very complex task, requiring years of training to gain the expertise and experience to do well.

Even with this extensive training, there can be substantial variability in the diagnoses given by different pathologists for the same patient, which can lead to misdiagnoses. For example, agreement in diagnosis for some forms of breast cancer can be as low as 48%, and similarly low for prostate cancer. The lack of agreement is not surprising given the massive amount of information that must be reviewed in order to make an accurate diagnosis. Pathologists are responsible for reviewing all the biological tissues visible on a slide. However, there can be many slides per patient, each of which is 10+ gigapixels when digitized at 40X magnification. Imagine having to go through a thousand 10 megapixel (MP) photos, and having to be responsible for every pixel. Needless to say, this is a lot of data to cover, and often time is limited.

To address these issues of limited time and diagnostic variability, we are investigating how deep learning can be applied to digital pathology, by creating an automated detection algorithm that can naturally complement pathologists’ workflow. We used images (graciously provided by the Radboud University Medical Center) which have also been used for the 2016 ISBI Camelyon Challenge1 to train algorithms that were optimized for localization of breast cancer that has spread (metastasized) to lymph nodes adjacent to the breast.

The results? Standard “off-the-shelf” deep learning approaches like Inception (aka GoogLeNet) worked reasonably well for both tasks, although the tumor probability prediction heatmaps produced were a bit noisy. After additional customization, including training networks to examine the image at different magnifications (much like what a pathologist does), we showed that it was possible to train a model that either matched or exceeded the performance of a pathologist who had unlimited time to examine the slides.
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.
In fact, the prediction heatmaps produced by the algorithm had improved so much that the localization score (FROC) for the algorithm reached 89%, which significantly exceeded the score of 73% for a pathologist with no time constraint2. We were not the only ones to see promising results, as other groups were getting scores as high as 81% with the same dataset. Even more exciting for us was that our model generalized very well, even to images that were acquired from a different hospital using different scanners. For full details, see our paper “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.
While these results are promising, there are a few important caveats to consider.
  • 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.
Training models is just the first of many steps in translating interesting research to a real product. From clinical validation to regulatory approval, much of the journey from “bench to bedside” still lies ahead — but we are off to a very promising start, and we hope by sharing our work, we will be able to accelerate progress in this space.



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.


Deep Learning for Detection of Diabetic Eye Disease



Diabetic retinopathy (DR) is the fastest growing cause of blindness, with nearly 415 million diabetic patients at risk worldwide. If caught early, the disease can be treated; if not, it can lead to irreversible blindness. Unfortunately, medical specialists capable of detecting the disease are not available in many parts of the world where diabetes is prevalent. We believe that Machine Learning can help doctors identify patients in need, particularly among underserved populations.

A few years ago, several of us began wondering if there was a way Google technologies could improve the DR screening process, specifically by taking advantage of recent advances in Machine Learning and Computer Vision. In "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs", published today in JAMA, we present a deep learning algorithm capable of interpreting signs of DR in retinal photographs, potentially helping doctors screen more patients in settings with limited resources.

One of the most common ways to detect diabetic eye disease is to have a specialist examine pictures of the back of the eye (Figure 1) and rate them for disease presence and severity. Severity is determined by the type of lesions present (e.g. microaneurysms, hemorrhages, hard exudates, etc), which are indicative of bleeding and fluid leakage in the eye. Interpreting these photographs requires specialized training, and in many regions of the world there aren’t enough qualified graders to screen everyone who is at risk.
Figure 1. Examples of retinal fundus photographs that are taken to screen for DR. The image on the left is of a healthy retina (A), whereas the image on the right is a retina with referable diabetic retinopathy (B) due a number of hemorrhages (red spots) present.
Working closely with doctors both in India and the US, we created a development dataset of 128,000 images which were each evaluated by 3-7 ophthalmologists from a panel of 54 ophthalmologists. This dataset was used to train a deep neural network to detect referable diabetic retinopathy. We then tested the algorithm’s performance on two separate clinical validation sets totalling ~12,000 images, with the majority decision of a panel 7 or 8 U.S. board-certified ophthalmologists serving as the reference standard. The ophthalmologists selected for the validation sets were the ones that showed high consistency from the original group of 54 doctors.

Performance of both the algorithm and the ophthalmologists on a 9,963-image validation set are shown in Figure 2.
Figure 2. Performance of the algorithm (black curve) and eight ophthalmologists (colored dots) for the presence of referable diabetic retinopathy (moderate or worse diabetic retinopathy or referable diabetic macular edema) on a validation set consisting of 9963 images. The black diamonds on the graph correspond to the sensitivity and specificity of the algorithm at the high sensitivity and high specificity operating points.
The results show that our algorithm’s performance is on-par with that of ophthalmologists. For example, on the validation set described in Figure 2, the algorithm has a F-score (combined sensitivity and specificity metric, with max=1) of 0.95, which is slightly better than the median F-score of the 8 ophthalmologists we consulted (measured at 0.91).

These are exciting results, but there is still a lot of work to do. First, while the conventional quality measures we used to assess our algorithm are encouraging, we are working with retinal specialists to define even more robust reference standards that can be used to quantify performance. Furthermore, interpretation of a 2D fundus photograph, which we demonstrate in this paper, is only one part in a multi-step process that leads to a diagnosis for diabetic eye disease. In some cases, doctors use a 3D imaging technology, Optical Coherence Tomography (OCT), to examine various layers of a retina in detail. Applying machine learning to this 3D imaging modality is already underway, led by our colleagues at DeepMind. In the future, these two complementary methods might be used together to assist doctors in the diagnosis of a wide spectrum of eye diseases.

Automated DR screening methods with high accuracy have the strong potential to assist doctors in evaluating more patients and quickly routing those who need help to a specialist. We are working with doctors and researchers to study the entire process of screening in settings around the world, in the hopes that we can integrate our methods into clinical workflow in a manner that is maximally beneficial. Finally, we are working with the FDA and other regulatory agencies to further evaluate these technologies in clinical studies.

Given the many recent advances in deep learning, we hope our study will be just one of many compelling examples to come demonstrating the ability of machine learning to help solve important problems in medical imaging in healthcare more broadly.

Learn more about the Health Research efforts of the Brain team at Google