Pathology Annotation Consensus & Inter-Rater Agreement

Before any digital-pathology AI model can be trained, someone has to decide what "tumor" looks like on a slide — and different pathologists don't always agree. This simulator stacks several independent raters' tumor calls over the same grid of whole-slide-image patches as floating 3D layers above the true tumor mask, then majority-votes them into a consensus ground truth. Live readouts compute Fleiss' kappa across the raters, the Dice overlap between the consensus mask and the real truth, and the fraction of patches every rater agreed on — showing directly why noisy or biased annotation, not model architecture, is often the ceiling on a pathology AI pipeline's real-world accuracy.