NuJEV: Structured Visual Judgments for Consistent Nuclei Quantification

Yonghuang Wu, Pengfei Wang, Zhenyang Liang, Yize Ma, Pengfei Song +2

Nuclear quantification requires locating and typing nuclei, beyond generating a count. Inspired by Jev's structured decisions in place of free-form generation, we introduce NuJEV for nuclear perception in Qwen3-VL-2B. We jointly train visual LoRA and a parallel head to predict nuclear presence, centers, and types. One image encoding feeds both the nuclear head and original language decoder. The decoder interprets region and type requests, with counts computed from the predicted nuclei. On 2,732 PanNuke test images, NuJEV achieves a five-query mean absolute error ($\mathrm{MAE}_5$) of 1.844 overall and 5.268 on dense images, with localization/typed F1 of 0.816/0.682. A matched encoder ablation shows 41.0\% lower dense error with visual adaptation. For language requests, NuJEV reduces request MAE by 76.2\% relative to direct generation. Our code will be released as open source.