Feature Robustness and Sex Differences in Medical Imaging: A Case Study in MRI-Based Alzheimer’s Disease Detection
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Feature Robustness and Sex Differences in Medical Imaging : A Case Study in MRI-Based Alzheimer’s Disease Detection. / Alzheimer’s Disease Neuroimaging Initiative.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference Singapore, September 18–22, 2022 Proceedings. ed. / Linwei Wang; Qi Dou; P. Thomas Fletcher; Stefanie Speidel; Shuo Li. Vol. Part 1 Springer, 2022. p. 88-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13431 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Feature Robustness and Sex Differences in Medical Imaging
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
AU - Petersen, Eike
AU - Feragen, Aasa
AU - da Costa Zemsch, Maria Luise
AU - Henriksen, Anders
AU - Wiese Christensen, Oskar Eiler
AU - Ganz, Melanie
AU - Alzheimer’s Disease Neuroimaging Initiative
N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Alzheimer’s disease
KW - Deep learning
KW - MRI
KW - Robustness
UR - http://www.scopus.com/inward/record.url?scp=85138794116&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16431-6_9
DO - 10.1007/978-3-031-16431-6_9
M3 - Article in proceedings
AN - SCOPUS:85138794116
SN - 9783031164309
VL - Part 1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 88
EP - 98
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
PB - Springer
Y2 - 18 September 2022 through 22 September 2022
ER -
ID: 322571686