TY - GEN
T1 - A Study of Age and Sex Bias in Multiple Instance Learning Based Classification of Acute Myeloid Leukemia Subtypes
AU - Sadafi, Ario
AU - Hehr, Matthias
AU - Navab, Nassir
AU - Marr, Carsten
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - Accurate classification of Acute Myeloid Leukemia (AML) subtypes is crucial for clinical decision-making and patient care. In this study, we investigate the potential presence of age and sex bias in AML subtype classification using Multiple Instance Learning (MIL) architectures. To that end, we train multiple MIL models using different levels of sex imbalance in the training set and excluding certain age groups. To assess the sex bias, we evaluate the performance of the models on male and female test sets. For age bias, models are tested against underrepresented age groups in the training data. We find a significant effect of sex and age bias on the performance of the model for AML subtype classification. Specifically, we observe that females are more likely to be affected by sex imbalance dataset and certain age groups, such as patients with 72 to 86 years of age with the RUNX1::RUNX1T1 genetic subtype, are significantly affected by an age bias present in the training data. Ensuring inclusivity in the training data is thus essential for generating reliable and equitable outcomes in AML genetic subtype classification, ultimately benefiting diverse patient populations.
AB - Accurate classification of Acute Myeloid Leukemia (AML) subtypes is crucial for clinical decision-making and patient care. In this study, we investigate the potential presence of age and sex bias in AML subtype classification using Multiple Instance Learning (MIL) architectures. To that end, we train multiple MIL models using different levels of sex imbalance in the training set and excluding certain age groups. To assess the sex bias, we evaluate the performance of the models on male and female test sets. For age bias, models are tested against underrepresented age groups in the training data. We find a significant effect of sex and age bias on the performance of the model for AML subtype classification. Specifically, we observe that females are more likely to be affected by sex imbalance dataset and certain age groups, such as patients with 72 to 86 years of age with the RUNX1::RUNX1T1 genetic subtype, are significantly affected by an age bias present in the training data. Ensuring inclusivity in the training data is thus essential for generating reliable and equitable outcomes in AML genetic subtype classification, ultimately benefiting diverse patient populations.
KW - Acute Myeloid Leukemia
KW - Age Bias
KW - Fairness
KW - Multiple Instance Learning
KW - Sex Bias
UR - http://www.scopus.com/inward/record.url?scp=85175824232&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-45249-9_25
DO - 10.1007/978-3-031-45249-9_25
M3 - Conference contribution
AN - SCOPUS:85175824232
SN - 9783031452482
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 256
EP - 265
BT - Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging - 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023, Proceedings
A2 - Wesarg, Stefan
A2 - Oyarzun Laura, Cristina
A2 - Puyol Antón, Esther
A2 - King, Andrew P.
A2 - Baxter, John S.H.
A2 - Erdt, Marius
A2 - Drechsler, Klaus
A2 - Freiman, Moti
A2 - Chen, Yufei
A2 - Rekik, Islem
A2 - Eagleson, Roy
A2 - Feragen, Aasa
A2 - Cheplygina, Veronika
A2 - Ganz-Benjaminsen, Melani
A2 - Ferrante, Enzo
A2 - Glocker, Ben
A2 - Moyer, Daniel
A2 - Petersen, Eikel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Workshop on Clinical Image-Based Procedures, CLIP 2023, 1st MICCAI Workshop on Fairness of AI in Medical Imaging, FAIMI 2023, held in conjunction with MICCAI 2023 and 2nd MICCAI Workshop on the Ethical and Philosophical Issues in Medical Imaging, EPIMI 2023
Y2 - 12 October 2023 through 12 October 2023
ER -