Compact and Structurally Transparent Cervical Cytology with Geometry-Driven Features and Closed-Form Attention

Cervical cytology is practiced as a sequence of roles: screen-ing decides where to look, careful reading determines what is present,and reporting accounts for the decision. We translate this workflow intoa compact and structurally transparent model. Screening is cast as afree–energy allocation over four explicit, cytology–native concepts (nu-clear–envelope roughness, orientation disorder, local N/C ratio, chro-matin heterogeneity), yielding a closed-form spatial attention as a soft-max of negative energy. Reading is handled by an analytic backbonebuilt from fixed geometric responses, nonnegative near–diagonal chan-nel mixing, and convex residual updates, producing stable and auditablefeatures. A probabilistic readout with a linear head then provides anintrinsic evidence–to–logit accounting without post–hoc explainers. De-spite only 2.2M parameters, the model achieves state-of-the-art accuracyon DSCC (93.2% ACC), competitive results on SIPaKMeD (98.5% ACC)and Herlev (76.8% ACC), and favorable latency. Source code is publiclyavailable at https://github.com/Dichao-Liu/GeoCEAN.