Automated acquisition of explainable knowledge from unannotated colposcopic images for predicting the natural course of <scp>CIN2</scp>

Machine learning models based on colposcopic images can effectively predict CIN2 prognosis, offering an objective tool for risk assessment and individualized clinical management. Different staining modalities provide complementary information, aiding in balancing progression risk reduction with overtreatment avoidance.