AWS for Industries

Tag: medical imaging

Executive Conversations: Realizing the Potential of Cloud Technology in Healthcare

Executive Conversations: Realizing the Potential of Cloud Technology in Healthcare

Vignesh Shetty, Senior Vice President and General Manager of Edison AI & Platform at GE Healthcare, joins Taha Kass-Hout, MD, MS Vice President and Chief Medical Officer at Amazon Web Services (AWS), to discuss strategies for building a modern data strategy to fuel innovation and improve patient care. GE Healthcare provides medical technology, pharmaceutical diagnostics, […]

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Scalable Medical Computer Vision Model Training with Amazon SageMaker Part 2

Scalable Medical Computer Vision Model Training with Amazon SageMaker Part 2

Introduction Training medical computer vision (CV) models requires a scalable compute and storage infrastructure. Training a medical CV model is unique compared to training a CV model in other domains, as we described in the first part of this blog series. In this second post, we show you how we scale a medical semantic segmentation training […]

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Scalable Medical Computer Vision Model Training with Amazon SageMaker Part 1

Scalable Medical Computer Vision Model Training with Amazon SageMaker Part 1

Introduction Computer vision (CV) and machine learning (ML) has been playing an increasingly import role in the field of medical imaging and radiology in the past decade. CV is the field that uses computers and programs to replicate human vision abilities such as recognition, classification, detection and measurement. Thanks to the advancement of deep learning […]

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Invicro Improves Medical Image Quality Prediction with SageMaker HPO Jobs

Invicro Improves Medical Image Quality Prediction with SageMaker HPO Jobs

Blog guest authored by Brian Avants, and Jacob Hesterman of Invicro, a REALM IDx company High-quality medical imaging data is a clinical necessity. Yet, there are few medical datasets with quality annotations that can be used to train models which automate an objective quality prediction process. Invicro, a REALM IDx company, worked with the Amazon […]

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Seamlessly and securely integrate on-premise medical image workflows into AWS

Seamlessly and securely integrate on-premise medical image workflows into AWS

Because healthcare organizations depend on their medical images to help improve patient outcomes, being able to securely store and retrieve those images reliably and quickly is crucial. Organizations face an increasing challenge to support a durable storage solution as image stores grow into hundreds of terabytes, and in some cases, petabytes. With the increasing number of ransomware […]

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Philips Lumify

AWS is How: Philips makes medical diagnostics accessible to more people

About half the world’s population has no access to basic healthcare. There are a number of reasons for this, including poverty, language barriers, and geography. Even in the United States, millions of people—particularly in rural areas—live more than 30 minutes away from the nearest hospital. This makes it difficult for them to get critical care […]

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Training Machine Learning Models on Multimodal Health Data with Amazon SageMaker

Training Machine Learning Models on Multimodal Health Data with Amazon SageMaker

This post was co-authored by Olivia Choudhury, PhD, Partner Solutions Architect; Michael Hsieh, Sr. AI/ML Specialist Solutions Architect; and Andy Schuetz, PhD, Sr. Startup Solutions Architect at AWS. This is the second blog post in a two-part series on Multimodal Machine Learning (Multimodal ML). In part one, we deployed pipelines for processing RNA sequence data, clinical […]

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Example visualization of a CT scan, with lung tumor mask overlaid in yellow

Building Scalable Machine Learning Pipelines for Multimodal Health Data on AWS

This post was co-authored by Olivia Choudhury, PhD, Partner Solutions Architect; Michael Hsieh, Senior AI/ML Specialist Solutions Architect; and Andy Schuetz, PhD, Sr. Partner Solutions Architect. Healthcare and life sciences organizations use machine learning (ML) to enable precision medicine, anticipate patient preferences, detect disease, improve care quality, and understand inequities. Rapid growth in health information […]

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