Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection. (arXiv:2204.01737v1 [eess.IV])

Convolutional neural networks have enabled significant improvements in
medical image-based disease classification. It has, however, become
increasingly clear that these models are susceptible to performance degradation
due to spurious correlations and dataset shifts, which may lead to
underperformance on underrepresented patient groups, among other problems. In
this paper, we compare two classification schemes on the ADNI MRI dataset: a
very simple logistic regression model that uses manually selected volumetric
features as inputs, and a convolutional neural network trained on 3D …

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