Case Studies Stanford Medicine Uses Snorkel to Revolutionize Medical Imaging Data Labeling

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	Stanford Medicine Uses Snorkel to Revolutionize Medical Imaging Data Labeling

Stanford Medicine Uses Snorkel to Revolutionize Medical Imaging Data Labeling

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Healthcare & Hospitals Life Sciences
Applicable Functions	Product Research & Development Quality Assurance
Use Cases	Automated Disease Diagnosis Clinical Image Analysis Remote Patient Monitoring
Services	Data Science Services System Integration
Challenge	Labeling training data for triaging models in medical imaging is a time-consuming process, often requiring person-months to person-years of radiologist time. This manual labeling is not only labor-intensive but also prone to human error, which can affect the accuracy and reliability of the models. The challenge was to find a more efficient and accurate method to label large datasets of medical images, which are crucial for developing and training machine learning models for disease diagnosis and patient monitoring. Read More
About Customer	Stanford Medicine is a leading academic medical center that integrates research, medical education, and healthcare. Known for its cutting-edge research and innovative approaches to medical challenges, Stanford Medicine collaborates with various institutions to advance the field of medicine. The institution is committed to improving patient care through the development and application of new technologies. In this case, Stanford Medicine partnered with Snorkel to enhance their data labeling processes for medical imaging, aiming to improve the efficiency and accuracy of their machine learning models used in disease diagnosis and patient monitoring. Read More
Solution	Stanford Medicine deployed a cross-modal Snorkel pipeline to automate the labeling of medical imaging datasets. This innovative approach allowed them to replace the traditional manual labeling process, which took person-months to person-years, with a more efficient method that completed the task in just a few hours. The Snorkel pipeline was able to match or even exceed the performance of manually gathered labels, ensuring high accuracy and reliability. This solution not only saved significant time and resources but also improved the overall quality of the labeled data, which is essential for training effective machine learning models. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Healthcare & Hospitals

Life Sciences

Applicable Functions

Product Research & Development

Quality Assurance

Use Cases

Automated Disease Diagnosis

Clinical Image Analysis

Remote Patient Monitoring

Services

Data Science Services

System Integration

Challenge

Labeling training data for triaging models in medical imaging is a time-consuming process, often requiring person-months to person-years of radiologist time. This manual labeling is not only labor-intensive but also prone to human error, which can affect the accuracy and reliability of the models. The challenge was to find a more efficient and accurate method to label large datasets of medical images, which are crucial for developing and training machine learning models for disease diagnosis and patient monitoring.

About Customer

Stanford Medicine is a leading academic medical center that integrates research, medical education, and healthcare. Known for its cutting-edge research and innovative approaches to medical challenges, Stanford Medicine collaborates with various institutions to advance the field of medicine. The institution is committed to improving patient care through the development and application of new technologies. In this case, Stanford Medicine partnered with Snorkel to enhance their data labeling processes for medical imaging, aiming to improve the efficiency and accuracy of their machine learning models used in disease diagnosis and patient monitoring.

Solution

Stanford Medicine deployed a cross-modal Snorkel pipeline to automate the labeling of medical imaging datasets. This innovative approach allowed them to replace the traditional manual labeling process, which took person-months to person-years, with a more efficient method that completed the task in just a few hours. The Snorkel pipeline was able to match or even exceed the performance of manually gathered labels, ensuring high accuracy and reliability. This solution not only saved significant time and resources but also improved the overall quality of the labeled data, which is essential for training effective machine learning models.

Impact #1	The deployment of the Snorkel pipeline significantly reduced the time required for labeling medical imaging datasets, replacing 8 person-months of manual labeling with just a few hours of automated processing.
Impact #2	The solution is currently being tested for deployment in Stanford and Department of Veteran Affairs (VA) hospital systems, indicating its potential for broader application and impact in the healthcare sector.
Impact #3	The automated labeling process ensured high accuracy and reliability, matching or exceeding the performance of manually gathered labels, which is crucial for developing effective machine learning models for disease diagnosis and patient monitoring.

Impact #1

The deployment of the Snorkel pipeline significantly reduced the time required for labeling medical imaging datasets, replacing 8 person-months of manual labeling with just a few hours of automated processing.

Impact #2

The solution is currently being tested for deployment in Stanford and Department of Veteran Affairs (VA) hospital systems, indicating its potential for broader application and impact in the healthcare sector.

Impact #3

The automated labeling process ensured high accuracy and reliability, matching or exceeding the performance of manually gathered labels, which is crucial for developing effective machine learning models for disease diagnosis and patient monitoring.

Benefit #1	8 Person-months of labeling replaced
Benefit #2	94% ROC AUC Performance
Benefit #3	50K+ Images labeled in minutes

Benefit #1

8 Person-months of labeling replaced

Benefit #2

94% ROC AUC Performance

Benefit #3

50K+ Images labeled in minutes

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Overview

Stanford Medicine Uses Snorkel to Revolutionize Medical Imaging Data Labeling

Operational Impact

Quantitative Benefit