About

1 minute to read

Welcome to the AI for Medical Imaging training course!

The course is a well-structured training material designed to help experienced Data Scientists start working in the Medical Imaging domain.

The course is designed for self-paced learning.

This course is organized as a structured set of links with short descriptions, comments, and explanations where they are needed.

In this course a lot of links to external resources are provided. These external materials should be used according to the Terms of Use and/or License specified by the authors of the content.

The Introduction chapter describes why we need AI in medicine, and what the challenges are in the medical imaging domain. The Medical Imaging chapter contains general information about the domain. The ongoing chapters describe how to work with images of a certain modality.

We will cover the most popular medical images modalities, such as:

• General Purpose X-ray

• Mammography

• CT

For each data source we consider the following aspects:

• How images are obtained (physics behind it, raw data format, etc.)

• How the images look

• How the images are used (medical value)

• How the images are stored (DICOM/NIfTI)

• Typical data preprocessing schemes

• Exercises and links to the data to play with

To avoid backtracking, it’s recommended to pass the course successively.