MEDSAE: DISSECTING MEDCLIP REPRESENTATIONS WITH SPARSE AUTOENCODERS
Unlocking Interpretability in Medical AI
This paper introduces Medical Sparse Autoencoders (MedSAEs) to enhance the interpretability of MedCLIP, a vision-language model for medical imaging. By training MedSAEs on chest X-ray embeddings, the researchers uncover 'monosemantic' neurons that align with clinically meaningful concepts like 'pulmonary edema' or 'pleural effusion.' An automated naming framework using MedGEMMA labels these features, achieving high interpretability accuracy. This approach provides a scalable method for building more transparent and trustworthy AI systems in healthcare, bridging high-performing models with human-understandable medical concepts, crucial for clinical reliability.
Executive Impact & Core Metrics
Our analysis reveals the profound impact of MedSAE on medical AI interpretability and transparency, enabling more reliable and clinically relevant AI systems.
21
Medically Meaningful Features Identified
0.70x
Minimum Detection Accuracy for Named Features
2.25
Improved Neuron Entropy (Lower is Better)
Key Takeaways for Enterprise AI Leaders
- MedSAEs successfully disentangle complex MedCLIP embeddings into human-interpretable medical concepts.
- Automated neuron naming via MedGEMMA provides accurate and clinically relevant labels for MedSAE features.
- The approach enhances model transparency and trustworthiness, critical for healthcare AI adoption.
- Identified features show high monosemanticity and stronger alignment with clinical concepts than raw MedCLIP embeddings.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Healthcare AI Interpretability
21
Distinct Medical Concepts Identified for Transparent AI
Enterprise Process Flow
Train MedSAE on MedCLIP Embeddings
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Assess Neuron Monosemanticity (Correlation)
→
Automated Interpretability & Naming (MedGEMMA)
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Validate via Detection Task
| Feature Type | Interpretability | Monosemanticity | Clinical Alignment |
|---|---|---|---|
| Raw MedCLIP Embeddings | Low | Lower Entropy (2.38) | Indirect |
| MedSAE Features | High (Named) | Higher Monosemanticity (2.25) | Strong & Direct |
Identifying Pulmonary Edema with MedSAE
One MedSAE neuron was precisely identified and named as 'Severe pulmonary edema with significant air trapping and subcutaneous emphysema' by MedGEMMA. This demonstrates the model's ability to isolate complex medical conditions into a single, interpretable feature, crucial for diagnostic support systems. The neuron showed a 0.82 detection accuracy, validating its semantic alignment.
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