Tag Archives: Health

Building better pangenomes to improve the equity of genomics

For decades, researchers worked together to assemble a complete copy of the molecular instructions for a human — a map of the human genome. The first draft was finished in 2000, but with several missing pieces. Even when a complete reference genome was achieved in 2022, their work was not finished. A single reference genome can’t incorporate known genetic variations, such as the variants for the gene determining whether a person has a blood type A, B, AB or O. Furthermore, the reference genome didn’t represent the vast diversity of human ancestries, making it less useful for detecting disease or finding cures for people from some backgrounds than others. For the past three years, we have been part of an international collaboration with 119 scientists across 60 institutions, called the Human Pangenome Research Consortium, to address these challenges by creating a new and more representative map of the human genome, a pangenome.

We are excited to share that today, in “A draft human pangenome reference”, published in Nature, this group is announcing the completion of the first human pangenome reference. The pangenome combines 47 individual genome reference sequences and better represents the genomic diversity of global populations. Building on Google’s deep learning technologies and past advances in genomics, we used tools based on convolutional neural networks (CNNs) and transformers to tackle the challenges of building accurate pangenome sequences and using them for genome analysis. These contributions helped the consortium build an information-rich resource for geneticists, researchers and clinicians around the world.

Using graphs to build pangenomes

In the typical analysis workflow for high-throughput DNA sequencing, a sequencing instrument reads millions of short pieces of an individual’s genome, and a program called a mapper or aligner then estimates where those pieces best fit relative to the single, linear human reference sequence. Next, variant caller software identifies the unique parts of the individual’s sequence relative to the reference.

But because humans carry a diverse set of sequences, sections that are present in an individual’s DNA but are not in the reference genome can’t be analyzed. One study of 910 African individuals found that a total of 300 million DNA base pairs — 10% of the roughly three billion base pair reference genome — are not present in the previous linear reference but occur in at least one of the 910 individuals.

To address this issue, the consortium used graph data structures, which are powerful for genomics because they can represent the sequences of many people simultaneously, which is needed to create a pangenome. Nodes in a graph genome contain the known set of sequences in a population, and paths through those nodes compactly describe the unique sequences of an individual’s DNA.

Schematic of a graph genome. Each color represents the sequence path of a different individual. Multiple paths passing through the same node indicate multiple individuals share that sequence, but some paths also show a single nucleotide variant (SNV), insertions, or deletions. Illustration credit Darryl Leja, National Human Genome Research Institute (NHGRI).

Actual graph genome for the major histocompatibility complex (MHC) region of the genome. Genes in MHC regions are essential to immune function and are associated with a person’s resistance and susceptibility to infectious disease and autoimmune disorders (e.g., ankylosing spondylitis and lupus). The graph shows the linear human genome reference (green) and different individual person’s sequence (gray).

Using graphs creates numerous challenges. They require reference sequences to be highly accurate and the development of new methods that can use their data structure as an input. However, new sequencing technologies (such as consensus sequencing and phased assembly methods) have driven exciting progress towards solving these problems.

Long-read sequencing technology, which reads larger pieces of the genome (10,000 to millions of DNA characters long) at a time, are essential to the creation of high quality reference sequences because larger pieces can be stitched together into assembled genomes more easily than the short pieces read out by earlier technologies. Short read sequencing reads pieces of the genome that are only 100 to 300 DNA characters long, but has been the highly scalable basis for high-throughput sequencing methods developed in the 2000s. Though long-read sequencing is newer and has advantages for reference genome creation, many informatics methods for short reads hadn’t been developed for long read technologies.

Evolving DeepVariant for error correction

Google initially developed DeepVariant, an open-source CNN variant caller framework that analyzes the short-read sequencing evidence of local regions of the genome. However, we were able to re-train DeepVariant to yield accurate analysis of Pacific Bioscience’s long-read data.

Training and evaluation schematic for DeepVariant.

We next teamed up with researchers at the University of California, Santa Cruz (UCSC) Genomics Institute to participate in a United States Food and Drug Administration competition for another long-read sequencing technology from Oxford Nanopore. Together, we won the award for highest accuracy in the nanopore category, with a single nucleotide variants (SNVs) accuracy that matched short-read sequencing. This work has been used to detect and treat genetic diseases in critically ill newborns. The use of DeepVariant on long-read technologies provided the foundation for the consortium’s use of DeepVariant for error correction of pangenomes.

DeepVariant’s ability to use multiple long-read sequencing modalities proved useful for error correction in the Telomere-to-Telomere (T2T) Consortium’s effort that generated the first complete assembly of a human genome. Completing this first genome set the stage to build the multiple reference genomes required for pangenomes, and T2T was already working closely with the Human Pangenome Project (with many shared members) to scale those practices.

With a set of high-quality human reference genomes on the horizon, developing methods that could use those assemblies grew in importance. We worked to adapt DeepVariant to use the pangenome developed by the consortium. In partnership with UCSC, we built an end-to-end analysis workflow for graph-based variant detection, and demonstrated improved accuracy across several thousand samples. The use of the pangenome allows many previously missed variants to be correctly identified.

Visualization of variant calls in the KCNE1 gene (a gene with variants associated with cardiac arrhythmias and sudden death) using a pangenome reference versus the prior linear reference. Each dot represents a variant call that is either correct (blue dot), incorrect (green dot) — when a variant is identified but is not really there —or a missed variant call (red dot). The top box shows variant calls made by DeepVariant using the pangenome reference while the bottom shows variant calls made by using the linear reference. Figure adapted from A Draft Human Pangenome Reference.

Improving pangenome sequences using transformers

Just as new sequencing technologies enabled new pangenome approaches, new informatics technologies enabled improvements for sequencing methods. Google adapted transformer architectures from analysis of human language to genome sequences to develop DeepConsensus. A key enabler for this was the development of a differentiable loss function that could handle the insertions and deletions common in sequencing data. This enabled us to have high accuracy without needing a decoder, allowing the speed required to keep up with terabytes of sequencer output.

Transformer architecture for DeepConsensus. DeepConsensus takes as input the repeated sequence of the DNA molecule, measured from fluorescent light detected by the addition of each base. DeepConsensus also uses as input the more detailed information about the sequencing process, including the duration of the light pulse (referred to here as pulse width or PW), the time between pulses (IP) the signal-to-noise ratio (SN) and which side of the double helix is being measured (strand).
Effect of alignment loss function in training evaluation of model output. Better accounting of insertions and deletions by a differentiable alignment function enables the model training process to better estimate errors.

DeepConsensus improves the yield and accuracy of instrument data. Because PacBio sequencing provides the primary sequence information for the 47 genome assemblies, we could apply DeepConsensus to improve those assemblies. With application of DeepConsensus, consortium members built a genome assembler that was able to reach 99.9997% assembly base-level accuracies.


We developed multiple new approaches to improve genetic sequencing methods, which we then used to construct pangenome references that enable more robust genome analysis.

But this is just the beginning of the story. In the next stage, a larger, worldwide group of scientists and clinicians will use this pangenome reference to study genetic diseases and make new drugs. And future pangenomes will represent even more individuals, realizing a vision summarized this way in a recent Nature story: “Every base, everywhere, all at once.” Read our post on the Keyword Blog to learn more about the human pangenome reference announcement.


Many people were involved in creating the pangenome reference, including 119 authors across 60 organizations, with the Human Pangenome Reference Consortium. This blog post highlights Google’s contributions to the broader work. We thank the research groups at UCSC Genomics Institute (GI) under Professors Benedict Paten and Karen Miga, genome polishing efforts of Arang Rhie at National Institute of Health (NIH), Genome Assembly and Polishing of Adam Phillipy’s group, and the standards group at National Institute of Standards and Technology (NIST) of Justin Zook. We thank Google contributors: Pi-Chuan Chang, Maria Nattestad, Daniel Cook, Alexey Kolesnikov, Anastaysia Belyaeva, and Gunjan Baid. We thank Lizzie Dorfman, Elise Kleeman, Erika Hayden, Cory McLean, Shravya Shetty, Greg Corrado, Katherine Chou, and Yossi Matias for their support, coordination, and leadership. Last but not least, thanks to the research participants that provided their DNA to help build the pangenome resource.

Source: Google AI Blog

Robust and efficient medical imaging with self-supervision

Despite recent progress in the field of medical artificial intelligence (AI), most existing models are narrow, single-task systems that require large quantities of labeled data to train. Moreover, these models cannot be easily reused in new clinical contexts as they often require the collection, de-identification and annotation of site-specific data for every new deployment environment, which is both laborious and expensive. This problem of data-efficient generalization (a model’s ability to generalize to new settings using minimal new data) continues to be a key translational challenge for medical machine learning (ML) models and has in turn, prevented their broad uptake in real world healthcare settings.

The emergence of foundation models offers a significant opportunity to rethink development of medical AI to make it more performant, safer, and equitable. These models are trained using data at scale, often by self-supervised learning. This process results in generalist models that can rapidly be adapted to new tasks and environments with less need for supervised data. With foundation models, it may be possible to safely and efficiently deploy models across various clinical contexts and environments.

In “Robust and Efficient MEDical Imaging with Self-supervision” (REMEDIS), to be published in Nature Biomedical Engineering, we introduce a unified large-scale self-supervised learning framework for building foundation medical imaging models. This strategy combines large scale supervised transfer learning with self-supervised learning and requires minimal task-specific customization. REMEDIS shows significant improvement in data-efficient generalization across medical imaging tasks and modalities with a 3–100x reduction in site-specific data for adapting models to new clinical contexts and environments. Building on this, we are excited to announce Medical AI Research Foundations (hosted by PhysioNet), an expansion of the public release of chest X-ray Foundations in 2022. Medical AI Research Foundations is a collection of open-source non-diagnostic models (starting with REMEDIS models), APIs, and resources to help researchers and developers accelerate medical AI research.

Large scale self-supervision for medical imaging

REMEDIS uses a combination of natural (non-medical) images and unlabeled medical images to develop strong medical imaging foundation models. Its pre-training strategy consists of two steps. The first involves supervised representation learning on a large-scale dataset of labeled natural images (pulled from Imagenet 21k or JFT) using the Big Transfer (BiT) method.

The second step involves intermediate self-supervised learning, which does not require any labels and instead, trains a model to learn medical data representations independently of labels. The specific approach used for pre-training and learning representations is SimCLR. The method works by maximizing agreement between differently augmented views of the same training example via a contrastive loss in a hidden layer of a feed-forward neural network with multilayer perceptron (MLP) outputs. However, REMEDIS is equally compatible with other contrastive self-supervised learning methods. This training method is applicable for healthcare environments as many hospitals acquire raw data (images) as a routine practice. While processes would have to be implemented to make this data usable within models (i.e., patient consent prior to gathering the data, de-identification, etc.), the costly, time-consuming, and difficult task of labeling that data could be avoided using REMEDIS.

REMEDIS leverages large-scale supervised learning using natural images and self-supervised learning using unlabeled medical data to create strong foundation models for medical imaging.

Given ML model parameter constraints, it is important that our proposed approach works when using both small and large model architecture sizes. To study this in detail, we considered two ResNet architectures with commonly used depth and width multipliers, ResNet-50 (1×) and ResNet-152 (2×) as the backbone encoder networks.

After pre-training, the model was fine-tuned using labeled task-specific medical data and evaluated for in-distribution task performance. In addition, to evaluate the data-efficient generalization, the model was also optionally fine-tuned using small amounts of out-of-distribution (OOD) data.

REMEDIS starts with representations initialized using large-scale natural image pretraining following the Big Transfer (BiT) method. We then adapt the model to the medical domain using intermediate contrastive self-supervised learning without using any labeled medical data. Finally, we fine-tune the model to specific downstream medical imaging tasks. We evaluate the ML model both in an in-distribution (ID) setting and in an out-of-distribution (OOD) setting to establish the data-efficient generalization performance of the model.

Evaluation and results

To evaluate the REMEDIS model’s performance, we simulate realistic scenarios using retrospective de-identified data across a broad range of medical imaging tasks and modalities, including dermatology, retinal imaging, chest X-ray interpretation, pathology and mammography. We further introduce the notion of data-efficient generalization, capturing the model’s ability to generalize to new deployment distributions with a significantly reduced need for expert annotated data from the new clinical setting. In-distribution performance is measured as (1) improvement in zero-shot generalization to OOD settings (assessing performance in an OOD evaluation set, with zero access to training data from the OOD dataset) and (2) significant reduction in the need for annotated data from the OOD settings to reach performance equivalent to clinical experts (or threshold demonstrating clinical utility). REMEDIS exhibits significantly improved in-distribution performance with up to 11.5% relative improvement in diagnostic accuracy over a strongly supervised baseline.

More importantly, our strategy leads to data-efficient generalization of medical imaging models, matching strong supervised baselines resulting in a 3–100x reduction in the need for retraining data. While SimCLR is the primary self-supervised learning approach used in the study, we also show that REMEDIS is compatible with other approaches, such as MoCo-V2, RELIC and Barlow Twins. Furthermore, the approach works across model architecture sizes.

REMEDIS outperformed the supervised baseline pre-trained on JFT-300M for various medical tasks and demonstrated improved data-efficient generalization, reducing data needs by 3–100x for adapting models to new clinical settings. This could potentially translate to significant reduction in clinician hours saved annotating data and cost of developing robust medical imaging systems.
REMEDIS is compatible with MoCo-V2, RELIC and Barlow Twins as alternate self-supervised learning strategies. All the REMEDIS variants lead to data-efficient generalization improvements over the strong supervised baseline for dermatology condition classification (T1), diabetic macular edema classification (T2), and chest X-ray condition classification (T3). The gray shaded area indicates the performance of the strong supervised baseline pre-trained on JFT.

Medical AI Research Foundations

Building on REMEDIS, we are excited to announce Medical AI Research Foundations, an expansion of the public release of chest X-ray Foundations in 2022. Medical AI Research Foundations is a repository of open-source medical foundation models hosted by PhysioNet. This expands the previous API-based approach to also encompass non-diagnostic models, to help researchers and developers accelerate their medical AI research. We believe that REMEDIS and the release of the Medical AI Research Foundations are a step toward building medical models that can generalize across healthcare settings and tasks.

We are seeding Medical AI Research Foundations with REMEDIS models for chest X-ray and pathology (with related code). Whereas the existing chest X-ray Foundation approach focuses on providing frozen embeddings for application-specific fine tuning from a model trained on several large private datasets, the REMEDIS models (trained on public datasets) enable users to fine-tune end-to-end for their application, and to run on local devices. We recommend users test different approaches based on their unique needs for their desired application. We expect to add more models and resources for training medical foundation models such as datasets and benchmarks in the future. We also welcome the medical AI research community to contribute to this.


These results suggest that REMEDIS has the potential to significantly accelerate the development of ML systems for medical imaging, which can preserve their strong performance when deployed in a variety of changing contexts. We believe this is an important step forward for medical imaging AI to deliver a broad impact. Beyond the experimental results presented, the approach and insights described here have been integrated into several of Google’s medical imaging research projects, such as dermatology, mammography and radiology among others. We’re using a similar self-supervised learning approach with our non-imaging foundation model efforts, such as Med-PaLM and Med-PaLM 2.

With REMEDIS, we demonstrated the potential of foundation models for medical imaging applications. Such models hold exciting possibilities in medical applications with the opportunity of multimodal representation learning. The practice of medicine is inherently multimodal and incorporates information from images, electronic health records, sensors, wearables, genomics and more. We believe ML systems that leverage these data at scale using self-supervised learning with careful consideration of privacy, safety, fairness and ethics will help lay the groundwork for the next generation of learning health systems that scale world-class healthcare to everyone.


This work involved extensive collaborative efforts from a multidisciplinary team of researchers, software engineers, clinicians, and cross-functional contributors across Google Health AI and Google Brain. In particular, we would like to thank our first co-author Jan Freyberg and our lead senior authors of these projects, Vivek Natarajan, Alan Karthikesalingam, Mohammad Norouzi and Neil Houlsby for their invaluable contributions and support. We also thank Lauren Winer, Sami Lachgar, Yun Liu and Karan Singhal for their feedback on this post and Tom Small for support in creating the visuals. Finally, we also thank the PhysioNet team for their support on hosting Medical AI Research Foundations. Users with questions can reach out to medical-ai-research-foundations at google.com.

Source: Google AI Blog

Detecting novel systemic biomarkers in external eye photos

Last year we presented results demonstrating that a deep learning system (DLS) can be trained to analyze external eye photos and predict a person’s diabetic retinal disease status and elevated glycated hemoglobin (or HbA1c, a biomarker that indicates the three-month average level of blood glucose). It was previously unknown that external eye photos contained signals for these conditions. This exciting finding suggested the potential to reduce the need for specialized equipment since such photos can be captured using smartphones and other consumer devices. Encouraged by these findings, we set out to discover what other biomarkers can be found in this imaging modality.

In “A deep learning model for novel systemic biomarkers in photos of the external eye: a retrospective study”, published in Lancet Digital Health, we show that a number of systemic biomarkers spanning several organ systems (e.g., kidney, blood, liver) can be predicted from external eye photos with an accuracy surpassing that of a baseline logistic regression model that uses only clinicodemographic variables, such as age and years with diabetes. The comparison with a clinicodemographic baseline is useful because risk for some diseases could also be assessed using a simple questionnaire, and we seek to understand if the model interpreting images is doing better. This work is in the early stages, but it has the potential to increase access to disease detection and monitoring through new non-invasive care pathways.

A model generating predictions for an external eye photo.

Model development and evaluation

To develop our model, we worked with partners at EyePACS and the Los Angeles County Department of Health Services to create a retrospective de-identified dataset of external eye photos and measurements in the form of laboratory tests and vital signs (e.g., blood pressure). We filtered down to 31 lab tests and vitals that were more commonly available in this dataset and then trained a multi-task DLS with a classification “head” for each lab and vital to predict abnormalities in these measurements.

Importantly, evaluating the performance of many abnormalities in parallel can be problematic because of a higher chance of finding a spurious and erroneous result (i.e., due to the multiple comparisons problem). To mitigate this, we first evaluated the model on a portion of our development dataset. Then, we narrowed the list down to the nine most promising prediction tasks and evaluated the model on our test datasets while correcting for multiple comparisons. Specifically, these nine tasks, their associated anatomy, and their significance for associated diseases are listed in the table below.

Prediction task       Organ system       Significance for associated diseases      
Albumin < 3.5 g/dL       Liver/Kidney       Indication of hypoalbuminemia, which can be due to decreased production of albumin from liver disease or increased loss of albumin from kidney disease.      
AST > 36.0 U/L       Liver      

Indication of liver disease (i.e., damage to the liver or biliary obstruction), commonly caused by viral infections, alcohol use, and obesity.

Calcium < 8.6 mg/dL       Bone / Mineral       Indication of hypocalcemia, which is most commonly caused by vitamin D deficiency or parathyroid disorders.      
eGFR < 60.0 mL/min/1.73 m2       Kidney      

Indication of chronic kidney disease, most commonly due to diabetes and high blood pressure.

Hgb < 11.0 g/dL       Blood count       Indication of anemia which may be due to blood loss, chronic medical conditions, or poor diet.      
Platelet < 150.0 103/µL       Blood count      

Indication of thrombocytopenia, which can be due to decreased production of platelets from bone marrow disorders, such as leukemia or lymphoma, or increased destruction of platelets due to autoimmune disease or medication side effects.

TSH > 4.0 mU/L       Thyroid       Indication of hypothyroidism, which affects metabolism and can be caused by many different conditions.      
Urine albumin/creatinine ratio (ACR) ≥ 300.0 mg/g       Kidney      

Indication of chronic kidney disease, most commonly due to diabetes and high blood pressure.

WBC < 4.0 103/µL       Blood count       Indication of leukopenia which can affect the body’s ability to fight infection.      

Key results

As in our previous work, we compared our external eye model to a baseline model (a logistic regression model taking clinicodemographic variables as input) by computing the area under the receiver operator curve (AUC). The AUC ranges from 0 to 100%, with 50% indicating random performance and higher values indicating better performance. For all but one of the nine prediction tasks, our model statistically outperformed the baseline model. In terms of absolute performance, the model’s AUCs ranged from 62% to 88%. While these levels of accuracy are likely insufficient for diagnostic applications, it is in line with other initial screening tools, like mammography and pre-screening for diabetes, used to help identify individuals who may benefit from additional testing. And as a non-invasive accessible modality, taking photographs of the external eye may offer the potential to help screen and triage patients for confirmatory blood tests or other clinical follow-up.

Results on the EyePACS test set, showing AUC performance of our DLS compared to a baseline model. The variable “n” refers to the total number of datapoints, and “N” refers to the number of positives. Error bars show 95% confidence intervals computed using the DeLong method. Indicates that the target was pre-specified as secondary analysis; all others were pre-specified as primary analysis.

The external eye photos used in both this and the prior study were collected using table top cameras that include a head rest for patient stabilization and produce high quality images with good lighting. Since image quality may be worse in other settings, we wanted to explore to what extent the DLS model is robust to quality changes, starting with image resolution. Specifically, we scaled the images in the dataset down to a range of sizes, and measured performance of the DLS when retrained to handle the downsampled images.

Below we show a selection of the results of this experiment (see the paper for more complete results). These results demonstrate that the DLS is fairly robust and, in most cases, outperforms the baseline model even if the images are scaled down to 150x150 pixels. This pixel count is under 0.1 megapixels, much smaller than the typical smartphone camera.

Effect of input image resolution. Top: Sample images scaled to different sizes for this experiment. Bottom: Comparison of the performance of the DLS (red) trained and evaluated on different image sizes and the baseline model (blue). Shaded regions show 95% confidence intervals computed using the DeLong method.

Conclusion and future directions

Our previous research demonstrated the promise of the external eye modality. In this work, we performed a more exhaustive search to identify the possible systemic biomarkers that can be predicted from these photos. Though these results are promising, many steps remain to determine whether technology like this can help patients in the real world. In particular, as we mention above, the imagery in our studies were collected using large tabletop cameras in a setting that controlled factors such as lighting and head positioning. Furthermore, the datasets used in this work consist primarily of patients with diabetes and did not have sufficient representation of a number of important subgroups – more focused data collection for DLS refinement and evaluation on a more general population and across subgroups will be needed before considering clinical use.

We are excited to explore how these models generalize to smartphone imagery given the potential reach and scale that this enables for the technology. To this end, we are continuing to work with our co-authors at partner institutions like Chang Gung Memorial Hospital in Taiwan, Aravind Eye Hospital in India, and EyePACS in the United States to collect datasets of imagery captured on smartphones. Our early results are promising and we look forward to sharing more in the future.


This work involved the efforts of a multidisciplinary team of software engineers, researchers, clinicians and cross functional contributors. Key contributors to this project include: Boris Babenko, Ilana Traynis, Christina Chen, Preeti Singh, Akib Uddin, Jorge Cuadros, Lauren P. Daskivich, April Y. Maa, Ramasamy Kim, Eugene Yu-Chuan Kang, Yossi Matias, Greg S. Corrado, Lily Peng, Dale R. Webster, Christopher Semturs, Jonathan Krause, Avinash V Varadarajan, Naama Hammel and Yun Liu. We also thank Dave Steiner, Yuan Liu, and Michael Howell for their feedback on the manuscript; Amit Talreja for reviewing code for the paper; Elvia Figueroa and the Los Angeles County Department of Health Services Teleretinal Diabetic Retinopathy Screening program staff for data collection and program support; Andrea Limon and Nikhil Kookkiri for EyePACS data collection and support; Dr. Charles Demosthenes for extracting the data and Peter Kuzmak for getting images for the VA data. Last but not least, a special thanks to Tom Small for the animation used in this blog post.

Source: Google AI Blog

Learning from deep learning: a case study of feature discovery and validation in pathology

When a patient is diagnosed with cancer, one of the most important steps is examination of the tumor under a microscope by pathologists to determine the cancer stage and to characterize the tumor. This information is central to understanding clinical prognosis (i.e., likely patient outcomes) and for determining the most appropriate treatment, such as undergoing surgery alone versus surgery plus chemotherapy. Developing machine learning (ML) tools in pathology to assist with the microscopic review represents a compelling research area with many potential applications.

Previous studies have shown that ML can accurately identify and classify tumors in pathology images and can even predict patient prognosis using known pathology features, such as the degree to which gland appearances deviate from normal. While these efforts focus on using ML to detect or quantify known features, alternative approaches offer the potential to identify novel features. The discovery of new features could in turn further improve cancer prognostication and treatment decisions for patients by extracting information that isn’t yet considered in current workflows.

Today, we’d like to share progress we’ve made over the past few years towards identifying novel features for colorectal cancer in collaboration with teams at the Medical University of Graz in Austria and the University of Milano-Bicocca (UNIMIB) in Italy. Below, we will cover several stages of the work: (1) training a model to predict prognosis from pathology images without specifying the features to use, so that it can learn what features are important; (2) probing that prognostic model using explainability techniques; and (3) identifying a novel feature and validating its association with patient prognosis. We describe this feature and evaluate its use by pathologists in our recently published paper, “Pathologist validation of a machine-learned feature for colon cancer risk stratification”. To our knowledge, this is the first demonstration that medical experts can learn new prognostic features from machine learning, a promising start for the future of this “learning from deep learning” paradigm.

Training a prognostic model to learn what features are important

One potential approach to identifying novel features is to train ML models to directly predict patient outcomes using only the images and the paired outcome data. This is in contrast to training models to predict “intermediate” human-annotated labels for known pathologic features and then using those features to predict outcomes.

Initial work by our team showed the feasibility of training models to directly predict prognosis for a variety of cancer types using the publicly available TCGA dataset. It was especially exciting to see that for some cancer types, the model's predictions were prognostic after controlling for available pathologic and clinical features. Together with collaborators from the Medical University of Graz and the Biobank Graz, we subsequently extended this work using a large de-identified colorectal cancer cohort. Interpreting these model predictions became an intriguing next step, but common interpretability techniques were challenging to apply in this context and did not provide clear insights.

Interpreting the model-learned features

To probe the features used by the prognostic model, we used a second model (trained to identify image similarity) to cluster cropped patches of the large pathology images. We then used the prognostic model to compute the average ML-predicted risk score for each cluster.

One cluster stood out for its high average risk score (associated with poor prognosis) and its distinct visual appearance. Pathologists described the images as involving high grade tumor (i.e., least-resembling normal tissue) in close proximity to adipose (fat) tissue, leading us to dub this cluster the “tumor adipose feature” (TAF); see next figure for detailed examples of this feature. Further analysis showed that the relative quantity of TAF was itself highly and independently prognostic.

A prognostic ML model was developed to predict patient survival directly from unannotated giga-pixel pathology images. A second image similarity model was used to cluster cropped patches of pathology images. The prognostic model was used to compute the average model-predicted risk score for each cluster. One cluster, dubbed the “tumor adipose feature” (TAF) stood out in terms of its high average risk score (associated with poor survival) and distinct visual appearance. Pathologists learned to identify TAF and pathologist scoring for TAF was shown to be prognostic.
Left: H&E pathology slide with an overlaid heatmap indicating locations of the tumor adipose feature (TAF). Regions highlighted in red/orange are considered to be more likely TAF by the image similarity model, compared to regions highlighted in green/blue or regions not highlighted at all. Right: Representative collection of TAF patches across multiple cases.

Validating that the model-learned feature can be used by pathologists

These studies provided a compelling example of the potential for ML models to predict patient outcomes and a methodological approach for obtaining insights into model predictions. However, there remained the intriguing questions of whether pathologists could learn and score the feature identified by the model while maintaining demonstrable prognostic value.

In our most recent paper, we collaborated with pathologists from the UNIMIB to investigate these questions. Using example images of TAF from the previous publication to learn and understand this feature of interest, UNIMIB pathologists developed scoring guidelines for TAF. If TAF was not seen, the case was scored as “absent”, and if TAF was observed, then “unifocal”, “multifocal”, and “widespread” categories were used to indicate the relative quantity. Our study showed that pathologists could reproducibly identify the ML-derived TAF and that their scoring for TAF provided statistically significant prognostic value on an independent retrospective dataset. To our knowledge, this is the first demonstration of pathologists learning to identify and score a specific pathology feature originally identified by an ML-based approach.

Putting things in context: learning from deep learning as a paradigm

Our work is an example of people “learning from deep learning”. In traditional ML, models learn from hand-engineered features informed by existing domain knowledge. More recently, in the deep learning era, a combination of large-scale model architectures, compute, and datasets has enabled learning directly from raw data, but this is often at the expense of human interpretability. Our work couples the use of deep learning to predict patient outcomes with interpretability methods, to extract new knowledge that could be applied by pathologists. We see this process as a natural next step in the evolution of applying ML to problems in medicine and science, moving from the use of ML to distill existing human knowledge to people using ML as a tool for knowledge discovery.

Traditional ML focused on engineering features from raw data using existing human knowledge. Deep learning enables models to learn features directly from raw data at the expense of human interpretability. Coupling deep learning with interpretability methods provides an avenue for expanding the frontiers of scientific knowledge by learning from deep learning.


This work would not have been possible without the efforts of coauthors Vincenzo L'Imperio, Markus Plass, Heimo Muller, Nicolò' Tamini, Luca Gianotti, Nicola Zucchini, Robert Reihs, Greg S. Corrado, Dale R. Webster, Lily H. Peng, Po-Hsuan Cameron Chen, Marialuisa Lavitrano, David F. Steiner, Kurt Zatloukal, Fabio Pagni. We also appreciate the support from Verily Life Sciences and the Google Health Pathology teams – in particular Timo Kohlberger, Yunnan Cai, Hongwu Wang, Kunal Nagpal, Craig Mermel, Trissia Brown, Isabelle Flament-Auvigne, and Angela Lin. We also appreciate manuscript feedback from Akinori Mitani, Rory Sayres, and Michael Howell, and illustration help from Abi Jones. This work would also not have been possible without the support of Christian Guelly, Andreas Holzinger, Robert Reihs, Farah Nader, the Biobank Graz, the efforts of the slide digitization team at the Medical University Graz, the participation of the pathologists who reviewed and annotated cases during model development, and the technicians of the UNIMIB team.

Source: Google AI Blog