.. _chap-about:

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 :ref:`chap-introduction` chapter describes why we need AI in medicine, and what the challenges are in the medical imaging domain.
The :ref:`chap-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:

- :ref:`chap-x-ray`
- :ref:`chap-mg`
- :ref:`chap-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.