# Higher-Order Domain Generalization in Magnetic Resonance-Based Assessment of Alzheimer's Disease

arXiv: https://arxiv.org/abs/2601.01485 · HTML: https://mlsanity.com/paper/2601.01485/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 52 references that are in the map.
- First posted on arXiv 4 Jan 2026.

## Builds on (most cited first, 21 of 21)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [Self-Challenging Improves Cross-Domain Generalization](https://mlsanity.com/paper/2007.02454/) (2020)
- [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020)
- [Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis](https://mlsanity.com/paper/1908.06912/) (2019)
- [Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization](https://mlsanity.com/paper/2203.07740/) (2022)
- [Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2306.05254/) (2023)
- [Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2206.13737/) (2022)
- [Adversarial Bayesian Augmentation for Single-Source Domain Generalization](https://mlsanity.com/paper/2307.09520/) (2023)
- [Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2307.09005/) (2023)
- [Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes](https://mlsanity.com/paper/2412.13573/) (2024)
- [Gradient-Guided Annealing for Domain Generalization](https://mlsanity.com/paper/2502.20162/) (2025)
- [Generalizing to Unseen Domains in Diabetic Retinopathy Classification](https://mlsanity.com/paper/2310.17255/) (2023)
- [Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum Synthesis](https://mlsanity.com/paper/2409.04768/) (2024)
- [PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization](https://mlsanity.com/paper/2505.12745/) (2025)
- [Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease](https://mlsanity.com/paper/2207.04574/) (2022)
- [ADRMX: Additive Disentanglement of Domain Features with Remix Loss](https://mlsanity.com/paper/2308.06624/) (2023)
- [ADAPT: Alzheimer Diagnosis through Adaptive Profiling Transformers](https://mlsanity.com/paper/2401.06349/) (2024)
- [Complex Style Image Transformations for Domain Generalization in Medical Images](https://mlsanity.com/paper/2406.00298/) (2024)
- [Distance Transform Guided Mixup for Alzheimer's Detection](https://mlsanity.com/paper/2505.22434/) (2025)

## Built on by (most cited first, 1 of 1)

- [LoV3D: Grounding Cognitive Prognosis Reasoning in Longitudinal 3D Brain MRI via Regional Volume Assessments](https://mlsanity.com/paper/2603.12071/) (2026)

## Abstract (arXiv)

Despite progress in deep learning for Alzheimer's disease (AD) diagnostics, models trained on structural magnetic resonance imaging (sMRI) often do not perform well when applied to new cohorts due to domain shifts from varying scanners, protocols and patient demographics. AD, the primary driver of dementia, manifests through progressive cognitive and neuroanatomical changes like atrophy and ventricular expansion, making robust, generalizable classification essential for real-world use. While convolutional neural networks and transformers have advanced feature extraction via attention and fusion techniques, single-domain generalization (SDG) remains underexplored yet critical, given the fragmented nature of AD datasets. To bridge this gap, we introduce Extended MixStyle (EM), a framework for blending higher-order feature moments (skewness and kurtosis) to mimic diverse distributional variations. Trained on sMRI data from the National Alzheimer's Coordinating Center (NACC; n=4,647) to differentiate persons with normal cognition (NC) from those with mild cognitive impairment (MCI) or AD and tested on three unseen cohorts (total n=3,126), EM yields enhanced cross-domain performance, improving macro-F1 on average by 2.4 percentage points over state-of-the-art SDG benchmarks, underscoring its promise for invariant, reliable AD detection in heterogeneous real-world settings. The source code will be made available upon acceptance at https://github.com/zobia111/Extended-Mixstyle.