Feature Robustness and Sex Differences in Medical Imaging: A Case Study in MRI-Based Alzheimer’s Disease Detection
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Submitted manuscript, 1.23 MB, PDF document
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.
|Title of host publication||Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 : 25th International Conference Singapore, September 18–22, 2022 Proceedings|
|Editors||Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li|
|Number of pages||11|
|Publication status||Published - 2022|
|Event||25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore|
Duration: 18 Sep 2022 → 22 Sep 2022
|Conference||25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022|
|Periode||18/09/2022 → 22/09/2022|
|Series||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Alzheimer’s disease, Deep learning, MRI, Robustness