Feature Robustness and Sex Differences in Medical Imaging: A Case Study in MRI-Based Alzheimer’s Disease Detection

Eike Petersen*, Aasa Feragen, Maria Luise da Costa Zemsch, Anders Henriksen, Oskar Eiler Wiese Christensen, Melanie Ganz, Alzheimer's Disease Neuroimaging Initiative

*Corresponding author af dette arbejde
15 Citationer (Scopus)

Abstract

Convolutional neural networks have enabled significant improvements in medical image-based diagnosis. It is, however, increasingly clear that these models are susceptible to performance degradation when facing spurious correlations and dataset shift, leading, e.g., to underperformance on underrepresented patient groups. In this paper, we compare two classification schemes on the ADNI MRI dataset: a simple logistic regression model using manually selected volumetric features, and a convolutional neural network trained on 3D MRI data. We assess the robustness of the trained models in the face of varying dataset splits, training set sex composition, and stage of disease. In contrast to earlier work in other imaging modalities, we do not observe a clear pattern of improved model performance for the majority group in the training dataset. Instead, while logistic regression is fully robust to dataset composition, we find that CNN performance is generally improved for both male and female subjects when including more female subjects in the training dataset. We hypothesize that this might be due to inherent differences in the pathology of the two sexes. Moreover, in our analysis, the logistic regression model outperforms the 3D CNN, emphasizing the utility of manual feature specification based on prior knowledge, and the need for more robust automatic feature selection.

OriginalsprogEngelsk
TitelMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
RedaktørerLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Antal sider11
ForlagSpringer Science and Business Media Deutschland GmbH
Publikationsdato2022
Sider88-98
ISBN (Trykt)9783031164309
DOI
StatusUdgivet - 2022
Begivenhed25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Varighed: 18 sep. 202222 sep. 2022

Konference

Konference25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Land/OmrådeSingapore
BySingapore
Periode18/09/202222/09/2022
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind13431 LNCS
ISSN0302-9743

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