# Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection

arXiv: https://arxiv.org/abs/2204.01737 · HTML: https://mlsanity.com/paper/2204.01737/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [Deep Structural Causal Models for Tractable Counterfactual Inference](https://mlsanity.com/paper/2006.06485/) (2020)
- [Why do deep convolutional networks generalize so poorly to small image transformations?](https://mlsanity.com/paper/1805.12177/) (2018)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [Reading Race: AI Recognises Patient's Racial Identity In Medical Images](https://mlsanity.com/paper/2107.10356/) (2021)
- [Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation](https://mlsanity.com/paper/1904.07773/) (2019)
- [Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification](https://mlsanity.com/paper/2011.06485/) (2020)
- [Assessing and tuning brain decoders: cross-validation, caveats, and guidelines](https://mlsanity.com/paper/1606.05201/) (2016)

## Built on by (most cited first, 19 of 19)

- [Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review](https://mlsanity.com/paper/2209.13177/) (2022)
- [FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification](https://mlsanity.com/paper/2303.08325/) (2023)
- [Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies](https://mlsanity.com/paper/2205.13421/) (2022)
- [Are Sex-based Physiological Differences the Cause of Gender Bias for Chest X-ray Diagnosis?](https://mlsanity.com/paper/2308.05129/) (2023)
- [Large language models improve Alzheimer's disease diagnosis using multi-modality data](https://mlsanity.com/paper/2305.19280/) (2023)
- [An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation](https://mlsanity.com/paper/2308.13415/) (2023)
- [Are generative models fair? A study of racial bias in dermatological image generation](https://mlsanity.com/paper/2501.11752/) (2025)
- [Bias in Unsupervised Anomaly Detection in Brain MRI](https://mlsanity.com/paper/2308.13861/) (2023)
- [(Predictable) Performance Bias in Unsupervised Anomaly Detection](https://mlsanity.com/paper/2309.14198/) (2023)
- [APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation](https://mlsanity.com/paper/2403.05114/) (2024)
- [Biasing & Debiasing based Approach Towards Fair Knowledge Transfer for Equitable Skin Analysis](https://mlsanity.com/paper/2405.10256/) (2024)
- [Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning](https://mlsanity.com/paper/2407.17543/) (2024)
- [Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification](https://mlsanity.com/paper/2509.09558/) (2025)
- [Understanding-informed Bias Mitigation for Fair CMR Segmentation](https://mlsanity.com/paper/2503.17089/) (2025)
- [Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast](https://mlsanity.com/paper/2603.04113/) (2026)
- [An Investigation Into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features](https://mlsanity.com/paper/2309.17197/) (2023)
- [Robustness and sex differences in skin cancer detection: logistic regression vs CNNs](https://mlsanity.com/paper/2504.11415/) (2025)
- [Effect of Demographic Bias on Skin Lesion Classification](https://mlsanity.com/paper/2606.03214/) (2026)
- [Metadata-Aware Adaptation of a Generative Foundation Model for Conditional CMR Synthesis](https://mlsanity.com/paper/2608.24342/) (2026)

## Abstract (arXiv)

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.