aceA WSI-Independent Inference Framework for Cervical and Urinary Cytology Using Hierarchical Labels
Background Cytology artificial intelligence (AI) systems often depend on whole-slide images (WSIs) or high-magnification cell images and are commonly optimized for a single specimen type. We developed CYTOLONE-v2, a contrastive language-image pretraining (CLIP)-based hierarchical classification framework for cervical and urinary cytology that is designed for individual-field inference without WSI input. Methods CYTOLONE-v1.1, previously trained for cervical cytology, was further trained by low-rank adaptation us…
Extending a cervical cytology model to urinary cytology gave 99.0% sensitivity for urothelial carcinoma at the Diagnosis level in single-field inference without whole-slide input.
The same urinary model had only 46.0% specificity against reactive change, and cervical accuracy fell at the Bethesda and Diagnosis levels after adaptation.
Findings are limited to selected WSI-derived fields and a reactive change versus urothelial carcinoma test cohort; external, case-level, direct-microscopy and prospective validation is still needed.
Evidence
- Peer-reviewedCytopathology2026-10-20
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Truvace Impact Record TRV-2026-2074, v1: “A WSI-Independent Inference Framework for Cervical and Urinary Cytology Using Hierarchical Labels.” Truvace, 2026-10-21. /record/TRV-2026-2074 (accessed at citation time). sha256 cfb854a5d36e92da…
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