Tissue Detection Determines False Positives in Diffusion-Based Histopathology Artifact Detection
How microscope image preprocessing secretly shapes what counts as 'normal' tissue
A step in preparing medical images for quality control—identifying which parts are actual tissue versus background—quietly determines which real samples get flagged as defects. Researchers found that switching to a better tissue-detection method dropped false alarms from 10% to 1.6%, simply by changing what the system learned to recognize as normal.
Pathologists rely on automated quality control to spot damaged slides before analysis begins. High false-positive rates force manual review of good slides, wasting time and resources; low rates risk missing real damage. This work shows that a seemingly boring preprocessing choice directly controls this trade-off, meaning labs that upgrade their tissue detection could cut false alarms tenfold without missing actual problems.