Hard-Negative Prototype Rectification for Low-Support Cervical Cytology Classification

Reliable cervical cytology classification remains difficult when rare diagnostic categories are represented by only a few labeled examples and exhibit substantial morphological overlap with neighboring classes. This study introduces HardNegRect, a lightweight inductive prototype-rectification module designed for low-support classification among fixed cervical cytology categories. The method constructs a class-specific hard-negative reference from the most similar competing prototypes and uses this inter-class context to predict a bounded, gated residual correction to each support-derived prototype. Because rectification depends exclusively on support information, the approach preserves independent query processing and avoids transductive access to the test distribution. HardNegRect was evaluated on two public cervical cytology benchmarks using common fold assignments, support sizes, held-out query sets, and draw-level metric aggregation for frozen-feature, metric-based, and optimization-based comparators. The study also includes a controlled component ablation study, a neighborhood-sensitivity analysis, and an additional-seed stability analysis. On Mendeley LBC, the clearest benefit occurred in the one-shot setting, where HardNegRect achieved a Macro-F1 of 0.9862±0.0062 and an SCC F1 of 0.9655±0.0216. On SIPaKMeD, the default Khn=2 configuration achieved Macro-F1 values of 0.9596±0.0052, 0.9626±0.0048, and 0.9616±0.0048 for K=1,3,10, respectively, numerically exceeding the strongest comparator mean at each support size. The controlled component ablation study associates the additional one-shot gain on Mendeley LBC with inter-class prototype correction rather than with embedding transformation alone. Overall, HardNegRect provides a lightweight, parameter-efficient, and geometry-aware extension to prototype-based low-support cytology classification, while patient-grouped, source-grouped, repeated-seed, and multi-center validation remain necessary before clinical generalization can be established.