CerDETR: Cell-Prior Empowered DETR for Cervical Lesion Detection

Cervical cancer remains a leading cause of female morbidityand mortality, making early lesion detection critical. Although RCNN-and YOLO-based detectors have improved performance, they still relyon complex post-processing and are limited in global reasoning. DETRoffers a new paradigm, but its direct application is challenged due to(1) large intra-class scale variation, (2) subtle inter-class differences, and(3) high inter-annotator variability. To address these challenges, we pro-pose CerDETR, a DETR-based framework empowered by cell priors. Itintroduces a training-only Prior Corrector branch to capture intra-classscale and inter-class feature variations. It employs an Auto MultiscalePrior Generator (Auto MPG) to produce precise multiscale cell priorscovering nuclei, cytoplasm, and clusters, an IoU and Contain-Guided(ICG) matching strategy for robust prior assignment under annotationnoise, and a Prior Query Enhance module to leverage lesion character-istics and optimize prior embeddings for discriminative feature learning.Experiments on four public datasets show that CerDETR consistentlyoutperforms RCNN-, YOLO-, and DETR-based methods, demonstrat-ing its effectiveness, generalization, and clinical potential. Source code isavailable at https://github.com/imAzhou/CerDETR.