The trust layer for CT restoration AI.
One engine — physics-grounded degradation ⇄ do-no-harm restoration ⇄ task-based evidence — powering three products: prove it, use it, teach it.
One engine, already validated in the notebook
The same two functions — degrade() and restore() — plus one scorer feed all three surfaces. A scan degraded to teach a student is generated by the exact pipeline that stress-tests a model and trains the restorer.
SafeRestore Studio
Conservative restoration that never ships a bare image — every output carries its evidence and a "needs review" flag. Revives older / low-dose scanners.
Evidence API
Vendor-neutral. Score any restoration model on 4 layers → Evidence Score, failure-case audit, risk memo. The independent layer before pilots & procurement.
SafeAI Academy
The same engine, reversed. Students learn why "looks sharper" isn't enough and spot where AI hallucinates — capstone runs Use + Prove live.
Restoration you can trust, because it ships with proof
Upload a real 3D CT folder (DICOM series) and it is restored by the actual trained DeblurUNet25D model when the local server is running — otherwise an in-browser phantom keeps the demo working offline. The server sorts the slices, denoises the volume with 2.5D neighbor context, and shows a representative denoised slice with volume-level improvement metrics. Crucially, the image always comes bundled with its evidence.
Evidence shipped with this image auto-generated
Score any restoration model — independently
Pick a model and run the validation protocol. The same 4 layers run on anyone's model; numbers below are the notebook's strict held-out runs (RSNA N=50 OOD, Mayo external, TotalSegmentator).
Evidence Score — all methods on the same OOD set
Composite of task-rescue, win-rate, low-iatrogenic, and fidelity. The point isn't "sharpest" — it's the best benefit/safety trade-off on data the model never saw.
Deep Evidence Pack V-Ultimate
Failure-case audit exposes risk, not hides it
Executive risk memo
The same engine becomes a classroom
Degradation isn't only for training models — it's how you train people. Students learn the one lesson that powers the whole product: a sharper-looking image can still be wrong.
🔍 Lab: spot the hallucination
One of these AI outputs invented a structure that is not in the ground truth. Which one is unsafe?
🎚 Lab: why "looks sharper" ≠ "is correct"
Drag to degrade the scan. Watch the downstream AI's confidence and the measured PSNR fall together — the eye is fooled long before the metric is.
Teaching point: this is exactly why SafeRestore validates with task-based evidence, not PSNR/SSIM alone.