Seminar : Fairness of Machine Learning in medical Image Analysis; Enzo FERRANTE

Medical institutions around the world are adopting machine learning
(ML) systems to assist in analyzing health data; at the same time, the
research community of fairness in ML has shown that these systems
can be biased, resulting in disparate performance for specific
subpopulations. In this talk, we will discuss the relationship between
bias, ML and health systems, addressing the specific case of gender
bias in X-ray classifiers [1] for computer-assisted diagnosis [2].

[1]: Gender imbalance in medical imaging datasets produces biased
classifiers for computer-aided diagnosis
A Larrazabal, N Nieto, V Peterson, D Milone and E Ferrante
Proceedings of the National Academy of Sciences (PNAS)

[2]: Addressing fairness in artificial intelligence for medical imaging
Ricci Lara MA, Echeveste E, Ferrante E
Nature Communications, 2022