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• The proposed two-stage framework combines unsupervised disease discovery with self-supervised disentanglement of implicit shape representations to separate pathological changes from physiological aging in 3D medical shapes.method 95%
• The method achieves near-supervised performance on ADNI hippocampus and OAI distal femur shapes, improving disentanglement and reconstruction over state-of-the-art unsupervised baselines.result 90%
• The framework enables high-fidelity reconstruction, controllable synthesis, and factor-based explainability of 3D medical shapes.technical 85%
• The method uses pseudo disease labels discovered through clustering on shape latent space without ground-truth diagnosis during discovery.method 90%
• The disentanglement is enforced using a multi-objective loss combining covariance and supervised contrastive loss with pseudo disease labels and ground truth age labels.technical 88%
• The method requires high-quality 3D medical imaging data which may not be available in all clinical settings.limitation 80%
• Model performance depends on the quality of unsupervised clustering which could introduce biases.limitation 75%
• The approach is particularly valuable for pharmaceutical companies to improve patient selection for drug trials by identifying disease-specific biomarkers.market 70%

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