More Data for Rare Disease Detection

In rare diseases, data is scarce and precious. Research teams need more samples to train reliable models without compromising privacy or fidelity. We partnered to build a safe pipeline that expands limited cohorts into rigorous training datasets for detection and progression modeling.

Context

Small, heterogeneous cohorts (medical images, labs) increase variance and overfitting risk. Labels are expensive; sharing is constrained. The goal: generate and curate additional training data so models generalize better while preserving patient privacy.

Approach

• Curation. Cohort harmonization, quality checks, and leakage prevention; de-identification and governance.
• Augmentation. Domain-aware transforms (intensity, geometry), mixup/cutout, and class-balancing strategies.
• Synthetic data. Generative models (GANs/diffusion/VAEs) tuned with clinician input; realism and diversity validated by blinded experts.
• Training + eval. Stratified cross-validation; calibration, sensitivity/specificity tracking; bias and drift checks.
• Safety. Privacy risk assessments (nearest-neighbor, membership inference), reproducible lineage, and review trails.

Impact

• Data volume: created substantially larger, balanced training sets from limited cohorts.
• Accuracy: improved sensitivity/ROC on held-out data with clinician-audited fidelity.
• Velocity: faster experimentation cycles with reusable curation + generation templates.

Note: engagement ongoing; metrics indicative of early pilots. All work follows strict privacy and ethics oversight.