AI-Use Cases / Reasons to do AI with Friends - Episode 2

The one where we tackle fibrosis

January 22, 2020 | 19:30 CET

  • 2 LECTURES
  • 45 MINUTES
  • 2 SPEAKERS
  • ESR MEMBERS Free
  • NON-MEMBERS Free

Learning Objectives

  1. To understand the difference between supervised and unsupervised learning
  2. To appreciate the need for large-scale data sets from different sites and scanners
  3. To understand how unsupervised algorithms can be validated

Use Case Description

Humans cannot identify complex patterns in CT data reliably. In the detection of fibrosis, relevant patterns need to be identified and we suspect that we don’t know all of them. We will write an algorithm that detects the relevant type of fibrosis and explain the difference between supervised and unsupervised learning.

Speakers

Presenter

Helmut Prosch

Vienna, Austria

Presenter

Georg Langs

Vienna, Austria

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