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🩻 Beyond a Single CNN: Ensembling, Uncertainty and Threshold Tuning

How weighted-averaging four pretrained CNNs, Monte Carlo Dropout uncertainty estimates and Youden's J threshold tuning combine into a more honest, clinically deployable pneumonia triage pipeline.

AI & Machine Learning3DAdvanced60 FPS

Four stylised CNN "readers" score the same chest X-ray, their votes are combined into a weighted ensemble, Monte Carlo Dropout resamples the network to visualise uncertainty, and a Youden's J-tuned threshold turns the probability into a clear/flag decision.

🔬 What It Demonstrates

Ensembling narrows the Monte Carlo Dropout uncertainty cloud compared to a single CNN, and moving the decision threshold along the ROC curve trades sensitivity for specificity — with borderline cases automatically flagged for human review.

🎮 How to Use

Pick a patient case and model mode, then adjust the MC Dropout passes, dropout rate and decision threshold. Watch the probability track, uncertainty cloud and ROC marker respond, or snap straight to the Youden's J optimum.

💡 Did You Know?

Monte Carlo Dropout, introduced by Gal & Ghahramani (2016), reuses a network's own dropout layers at inference time as a cheap approximation to Bayesian uncertainty — no retraining or extra models required.

⚙ Under the hood

How weighted-averaging four pretrained CNNs, Monte Carlo Dropout uncertainty estimates and Youden's J threshold tuning combine into a more honest, clinically deployable pneumonia triage pipeline.

artificial intelligencemachine learningneural networksdeep learningensemble methodsuncertainty quantificationthresholdingThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install