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arXiv Computer Vision By Ajeet Kumar Singh, Nehal Mishra, Anurag Tripathi, Santanu Chaudhury, Dorien Herremans

Learning to Reason Like a Cytopathologist:Clinically Grounded Multiscale Evidential Learning for Cervical Cytology

Read the original on arXiv Computer Vision →

The paper introduces a multiscale evidential learning framework for cervical cytology that models disease progression along an ordinal continuum using clinically grounded soft anchors. It encodes nuclear, cytoplasmic, and neighborhood cellular features separately with a shared DINOv2 backbone, then transforms calibrated predictions into Dempster–Shafer belief masses, fusing them with reliability discounting to explicitly handle conflicting or insufficient evidence. The resulting belief state enables interpretable routing that distinguishes confidently classified cases from those requiring further review, and demonstrates consistent generalization across two public cytology datasets with uncertainty correlating strongly with diagnostic difficulty.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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