geosae-brain-mri-sae

name: geosae-brain-mri-sae description: > GeoSAE methodology for interpretable brain MRI foundation model annotation using geometry-guided sparse autoencoders with age-deconfounded partial correlations. Prevents SAE feature collapse in deep transformer layers, extracts biomarkers from frozen brain MRI foundation models. Achieves MCI-to-AD conversion prediction (AUC 0.746) with 2% embedding dimensions, cross-cohort replication (r=0.97). Use when: GeoSAE, brain MRI foundation model interpretability, sparse autoencoder for medical imaging, Alzheimer's biomarker discovery, age-deconfounded analysis, SAE feature collapse prevention, geometric prior SAE, brain MRI annotation, ADNI AIBL MRI analysis, Braak staging localization, MCI conversion prediction.

GeoSAE: Geometry-Guided SAE for Brain MRI Foundation Model Annotation

Nerrise et al., Stanford University, arXiv:2605.01829 (May 2026) CVPR Workshop on Computer Vision for Clinical Applications (CV4Clinical) 2026

Problem

Brain MRI foundation models learn rich anatomical representations, but interpreting what clinical information they encode remains difficult. Standard SAEs suffer from severe feature collapse in deep transformer layers. In Alzheimer's research, aging confounds nearly every clinical variable, making naive annotation unreliable.

Core Method

GeoSAE uses the foundation model's learned manifold geometry to prevent feature collapse and annotates surviving features via age-deconfounded partial correlations.

Architecture

Brain MRI (T1-weighted)
    → Frozen Foundation Model (e.g., SynthSeg, FreeSurfer-style)
    → Layer-wise activations
    → GeoSAE (geometry-guided sparse autoencoder)
    → Interpretable features
    → Age-deconfounded partial correlation annotation
    → Clinical biomarker mapping

Key Innovations

1. Geometric prior guidance: Uses the manifold structure learned by the foundation model to guide SAE training, preventing feature collapse in deep layers
2. Age-deconfounded annotation: Partial correlations control for age, isolating disease-specific signals from normal aging effects
3. Cross-cohort replication: Features replicate across ADNI → AIBL without retraining (r=0.97)

Results

Key Findings

• Compact interpretable feature set predicts MCI-to-AD conversion using only 2% of embedding dimensions
• Comorbidity-annotated features achieve only chance-level performance, suggesting GeoSAE captures disease-specific rather than comorbid signals
• Neuroanatomical localization consistent with Braak staging of AD pathology
• Cross-cohort generalization without any retraining needed

Datasets

• ADNI: Alzheimer's Disease Neuroimaging Initiative
• AIBL: Australian Imaging Biomarkers and Lifestyle Study
• Total: ~14,000 T1-weighted MRI scans

Usage Patterns

1. Biomarker Discovery from Frozen Models

Apply GeoSAE to any frozen brain MRI foundation model to extract interpretable clinical biomarkers without retraining the base model.

2. Age-Deconfounded Clinical Analysis

Use partial correlation annotation to separate disease effects from normal aging, critical for neurodegenerative disease research.

3. Cross-Cohort Validation

Leverage geometry-guided features that replicate across different datasets without retraining, enabling multi-site biomarker validation.

4. SAE Feature Collapse Prevention

Use geometric priors from the foundation model's manifold structure to guide SAE training in deep transformer layers.

Limitations

• Requires a pretrained brain MRI foundation model
• T1-weighted MRI only (no multi-modal extension shown)
• Age deconfounding assumes linear age effects
• Focused on AD/MCI — extension to other diseases needs validation

Code

• https://github.com/favour-nerrise/GeoSAE

Related Work

• Sparse Autoencoders (SAEs) for LLM interpretability
• Brain MRI foundation models (SynthSeg, SynSegHD, etc.)
• Alzheimer's disease neuroimaging biomarkers
• Braak staging of AD pathology
• Age-deconfounded neuroimaging analysis