GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models
📰 ArXiv cs.AI
Learn to annotate brain MRI foundation models using GeoSAE, a geometric prior-guided layer-wise sparse autoencoder framework, to improve clinical information interpretation
Action Steps
- Apply GeoSAE to brain MRI foundation models to annotate clinically relevant features
- Use geometric priors to guide the sparse autoencoder and prevent feature collapse
- Evaluate the performance of GeoSAE in annotating brain MRI models compared to standard sparse autoencoders
- Integrate GeoSAE into existing Alzheimer's disease research pipelines to improve annotation reliability
- Test GeoSAE on various brain MRI datasets to assess its generalizability
Who Needs to Know This
Neuroimaging researchers and clinicians can benefit from this framework to better understand the clinical information encoded in brain MRI foundation models, particularly in Alzheimer's disease research
Key Insight
💡 GeoSAE uses geometric priors to prevent feature collapse in sparse autoencoders, enabling more reliable annotation of clinically relevant features in brain MRI foundation models
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🧠💻 GeoSAE: a geometric prior-guided SAE framework for annotating brain MRI foundation models #AI #Neuroimaging #Alzheimers
Key Takeaways
Learn to annotate brain MRI foundation models using GeoSAE, a geometric prior-guided layer-wise sparse autoencoder framework, to improve clinical information interpretation
Full Article
Title: GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models
Abstract:
arXiv:2605.01829v1 Announce Type: cross Abstract: Brain MRI foundation models learn rich representations of anatomy, but interpreting what clinical information they encode remains an open problem. Standard sparse autoencoders (SAEs) suffer from severe feature collapse in deep transformer layers, and in Alzheimer's disease (AD) research, aging confounds nearly every clinical variable, making naive annotation unreliable. We propose GeoSAE, a geometry-guided SAE framework that uses the foundation m
Abstract:
arXiv:2605.01829v1 Announce Type: cross Abstract: Brain MRI foundation models learn rich representations of anatomy, but interpreting what clinical information they encode remains an open problem. Standard sparse autoencoders (SAEs) suffer from severe feature collapse in deep transformer layers, and in Alzheimer's disease (AD) research, aging confounds nearly every clinical variable, making naive annotation unreliable. We propose GeoSAE, a geometry-guided SAE framework that uses the foundation m
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