Data Analytics in Medicine (DAIM)

Images Overview

Introduction

  • Welcome to the Data Analytics in Medicine (DAIM) course!
  • In the coming weeks, we will be covering a range of topics from basic data visualisation all the way up to machine learning.
  • There is a lot of exciting material to cover, so let’s jump straight in.

Introduction

As always:

  • Please let one of the team know if you have any questions during the course or any suggestions for improvement.

Overall Course Aim

  • To broaden your Python skills into fully-fledged and practical experience that can be used to:
    • Undertake research
    • Improve clinical services via smart audit
    • Build new next level clinical tools
  • And most importantly, to directly improve patient care.

Overall Course Philosophy

  • I set up this course as I want to accelerate the process of learning to code as a clinician after my own experiences.
  • The massive range of resources online can be intimidating.
  • Knowing how to improve your coding skills can be difficult.
  • We want people to leave this course with a toolkit to enable solving real world clinical problems.
  • We also want to improve the quality of cross-disciplinary work.

Tips to gain the most from DAIM

  • Practice, practice, practice
  • Unfortunately, there will be times when learning to code can get a little frustrating. But remember, we are here to help you through these hurdles.
  • Finding resources
    • Code documentation
    • Stack Overflow forum
    • GitHub code

Course breakdown

  • The course is split into multiple modules which each cover a different area.
  • Each module is split into:
    • Seminars - Lecture-style talks and discussions of the topic for the week.
    • Workshops - Practical exercises via Workbooks to gain hands-on experience, guided by direct or indirect support of our experienced tutors.
  • The workshops will use Jupyter Notebooks within Google Colab to deliver the content.

5-minute open discussion

  • Questions:
    • What incentive did you have for signing up for this course?
    • What is the one skill that you are hoping to gain?

Break!

Learning Levels

  • We’ve created a set of learning levels (LLs) for this course:
    1. Level 1 - Basic Practical Skills (coding skills)
    2. Level 2 - Theory (modelling skills)
    3. Level 3 - Project Planning and Management
    4. Level 4 - Clinical Translation

Module 2 - Course Introduction

  • Seminar content will include:
    • Aims and objectives of the course.
    • Overview of resources to use throughout the course.
    • Clinical applications of programming.

Module 2 - Core Python for Image Processing

  • Seminar content will include:
    • How images are digitally represented.
    • Basics of multiple Python packages.
      • PIL, a Python library for working with images.
      • NumPy, a Python library for working with large structured data.
  • Workshop content will include:
    • How to access and render different types of image data.

Module 3 - Python and DICOM

  • Seminar content will include:
    • What the DICOM standard is (think X-rays and CTs)
    • How computers represent scan data
    • What data is in a DICOM file
  • Workshop content will include:
    • Opening and rendering DICOM with Python
    • Extracting demographics from DICOM

Module 4 - AI for Medicine

  • Seminar content will include:
    • Different types of “machine learning”
    • The practical steps needed to take an idea to reality
    • Discussing advantages and limitations of these algorithms
  • Workshop content will include:
    • Preprocessing data for training
    • Constructing, training, and evaulating a basic neural network for pneumonia detection on CXR

Course Timeline

  • TODO (Make this when the updated course layout is better specified)

Course Feedback

  • We will be asking for course feedback to allow us to refine the course for further cohorts.
  • Please fill this out at the start and end of the course.
  • Please include honest feedback! Only with honest feedback can we further improve this course.

Note - Terminology

  • We will be using the term “programming” in this course to prevent confusion with:
    • Clinical coding - SNOMED-CT, ICD-10, etc…
    • Patients coding - crash trollies, etc…

Thank you!

Any questions?