CERVEX: A Foundation-Model Framework With Built-In Explainable AI for Automated Cervical Cytology Classification From Pap Smear Images

Cervical cancer is largely preventable when ab- normal cells are detected early. However, manual Pap-smear screening remains labor-intensive because a specimen may con- tain thousands of cells, borderline abnormalities are subtle, and expert cytology review is not equally accessible. Although artificial intelligence can reduce this workload, conventional classifiers often return a label without directly exposing the evidence used and may exploit staining or image-acquisition shortcuts. To address these limitations, we present CERVEX, a pathology-foundation-model framework with an additive class- evidence readout. Each class score is computed as the spatial mean of its class-specific evidence map plus a learned bias; therefore, the heatmap and numerical score are generated by the same forward calculation. CERVEX also produces a numerical research report containing class probabilities, evidence strength, ranked candidate regions, and segmentation-derived morphology, including N:C ratio and nuclear geometry. Under repeated slide- grouped evaluation on RIVA, with labelled target-cohort cells included during training, CERVEX distinguished abnormal from normal cells with AUROC 0.911 (SD 0.009), sensitivity 0.869, and specificity 0.796. Furthermore, fixed-grid analysis without cell coordinates distinguished low- from high-grade disease across 101 graded abnormal slides with AUROC 0.823 [0.728, 0.911]. On SIPaKMeD, nucleus and complete-cell segmentation reached Dice 0.932 and 0.942, while nuclear area fraction and N:C ratio reached measurement correlations of 0.977 and 0.962, respectively. Moreover, CERVEX produced class-specific spatial evidence while a separate held-out CRIC comparison did not resolve an accuracy difference from the conventional classifier, demonstrating computation-linked explainability with only 1.9% measured runtime overhead. Ultimately, CERVEX enables auto- mated analysis of cervical cytology microscope fields by linking cell classification, computation-linked spatial evidence, and in- terpretable numerical morphology indices in one pipeline. This supports transparent candidate-region review and establishes a practical foundation for future whole-slide screening and prospective clinical validation.