Lecture (Other)

FZJ-2024-06207

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Explainable AI in Medical Image Analysis

Pfaehler, E. (Corresponding author)FZJ* ; Krieger, L. (Corresponding author)FZJ*

2024

Abstract: The use of Artificial Intelligence (AI) in the medical domain is of high interest. AI could facilitate the work of physicians and guide them in their clinical decision-making. However, many AI-based methods are still a black box and hardly understood. As every patient has the right to an explainable diagnosis, it is important to understand and explain the processes and reasons behind the decisions of Convolutional Neural Networks (CNNs). In this workshop, we will explain the different applications of AI and Explainable AI in medical image analysis. The participants will learn about the different Explainable AI methods, their limitations, and how they could be included in a clinical workflow. Some Explainable AI methods will be applied to examples.

Note: Elisabeth Pfaehler is funded by the European Union, Marie-Curie Sklodowska Fellowship HORIZON-MSCA-2021-PF-01, grant 101068572.

---

Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)
2. Physik der Medizinischen Bildgebung (INM-4)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2024

---