Towards Aleatoric and Epistemic Uncertainty in Medical Image Classification

Löhr, Timo ; Ingrisch, Michael und Hüllermeier, Eyke (Juli 2024): Towards Aleatoric and Epistemic Uncertainty in Medical Image Classification. 22nd International Conference of Artificial Intelligence in Medicine (AIME 2024), Salt Lake City, Utah, USA, 9. - 12. July 2024. In: Artificial Intelligence in Medicine, LNAI Bd. 14845 Springer Cham. S. 145-155

Abstract

Medical domain applications require a detailed understanding of the decision making process, in particular when data-driven modeling via machine learning is involved, and quantifying uncertainty in the process adds trust and interpretability to predictive models. However, current uncertainty measures in medical imaging are mostly monolithic and do not distinguish between different sources and types of uncertainty. In this paper, we advocate the distinction between so-called aleatoric and epistemic uncertainty in the medical domain and illustrate its potential in clinical decision making for the case of PET/CT image classification.

• Dokumententyp: Konferenzbeitrag (Paper)
• Keywords: Medical Image Analysis, Machine Learning, Uncertainty Quantification, PET/CT Data
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme
• ISBN: 978-3-031-66534-9
• ISSN: 0302-9743
• Sprache: Englisch