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Applied Machine Learning for Healthcare and Life Sciences using AWS

You're reading from   Applied Machine Learning for Healthcare and Life Sciences using AWS Transformational AI implementations for biotech, clinical, and healthcare organizations

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781804610213
Length 224 pages
Edition 1st Edition
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Author (1):
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Ujjwal Ratan Ujjwal Ratan
Author Profile Icon Ujjwal Ratan
Ujjwal Ratan
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Table of Contents (19) Chapters Close

Preface 1. Part 1: Introduction to Machine Learning on AWS
2. Chapter 1: Introducing Machine Learning and the AWS Machine Learning Stack FREE CHAPTER 3. Chapter 2: Exploring Key AWS Machine Learning Services for Healthcare and Life Sciences 4. Part 2: Machine Learning Applications in the Healthcare Industry
5. Chapter 3: Machine Learning for Patient Risk Stratification 6. Chapter 4: Using Machine Learning to Improve Operational Efficiency for Healthcare Providers 7. Chapter 5: Implementing Machine Learning for Healthcare Payors 8. Chapter 6: Implementing Machine Learning for Medical Devices and Radiology Images 9. Part 3: Machine Learning Applications in the Life Sciences Industry
10. Chapter 7: Applying Machine Learning to Genomics 11. Chapter 8: Applying Machine Learning to Molecular Data 12. Chapter 9: Applying Machine Learning to Clinical Trials and Pharmacovigilance 13. Chapter 10: Utilizing Machine Learning in the Pharmaceutical Supply Chain 14. Part 4: Challenges and the Future of AI in Healthcare and Life Sciences
15. Chapter 11: Understanding Common Industry Challenges and Solutions 16. Chapter 12: Understanding Current Industry Trends and Future Applications 17. Index 18. Other Books You May Enjoy

Building a medical image classification model using SageMaker

One common application of ML in medical imaging is for classifying images into different categories. These categories can consist of different types of diseases determined by visual biomarkers. It can also recognize anomalies in a broad group of images and flag the ones that need further investigation by a radiologist. Having a classifier automate the task of prescreening the images reduces the burden on radiologists, who can concentrate on more complex and nuanced cases requiring expert intervention. In this exercise, we will train a model on SageMaker to recognize signs of pneumonia in a chest X-ray. Let us begin by acquiring the dataset.

Acquiring the dataset and code

The Chest X-Ray Images (Pneumonia) dataset is available on the Kaggle website here: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia?resource=download.

The dataset consists of 5,863 chest X-ray images in JPEG format organized...

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