Each glass slide is digitized by a line-scanner, screened for focus/staining artifacts at QC, then divided into an 8x8 grid of tissue patches. A simulated AI model assigns each patch a tumor probability built from a hidden "ground truth" tumor region plus noise representing model uncertainty and scanner/domain shift. Patches are classified positive once their probability crosses the decision threshold, then compared against ground truth at the validation gate to update a running confusion matrix.
predicted(i,j) = clip( truth(i,j)*0.75 + 0.15 + U(-1,1)*noise , 0, 1 )
class(i,j) = predicted(i,j) > threshold ? POS : NEG
Sensitivity = TP / (TP + FN)
Specificity = TN / (TN + FP)
- Scan throughput - sets how many slides per minute enter the belt.
- QC strictness - lowers the artifact-score threshold above which a slide is rejected before reaching the AI.
- AI decision threshold - the probability cutoff used to call a patch tumor-positive; moving it trades sensitivity for specificity.
- Model noise - injected prediction error simulating scanner variability or domain shift.
This 2D companion renders the same belt as a flat overhead schematic instead of a 3D scene, using the same statistics engine as the 3D version.