DICOM (Digital Imaging and Communications in Medicine) is an image format that contains visualizations of X-Rays and MRIs as well as any associated metadata. DICOM is the standard for medical professionals and healthcare researchers for visualizing and interpreting X-Rays and MRIs. The purpose of this post is to solve two problems:

Visualize and label DICOM images using a custom data labeling workflow on Amazon SageMaker Ground Truth, a fully managed data labeling service supporting built-in or custom data labeling workflows
Develop a DenseNet image classification model using the MONAI framework on Amazon SageMaker, a comprehensive and fully managed data science platform with purpose-built tools to prepare, build, train, and deploy machine learning (ML) models on the cloud

For this post, we use a chest X-Ray DICOM images dataset from the MIMIC Chest X-Ray (MIMIC-CXR) Database, a publicly available database of chest X-Ray images in DICOM format

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