2026-07-16 04:00 UTCOriginal source2 min readUpdated: 2026-07-16 08:21 UTC

C-Norm: Cell-Distribution Normalization Enables Precision Recognition of Medical-Cell Image

A new method called C-Norm addresses poor AI performance in cervical cancer screening by normalizing cell distribution in TCT images. It decouples abnormal and normal cells and re-synthesizes them for uniform distribution, then uses a hybrid YOLOv12-DINOv3 architecture for detection. Experiments show state-of-the-art results.

SourcearXiv Computer VisionAuthor: Yang Qianl, Liu Xiany, Dai Daw, Chen Jing, Shen Xiaoj, Fu Kaiw, Tang Ming, Zou Dongl

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[Submitted on 14 Jul 2026]

Title:C-Norm: Cell-Distribution Normalization Enables Precision Recognition of Medical-Cell Image

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Abstract:ThinPrep Cytologic Test (TCT) enables early cervical cancer screening, but manual reading is time-consuming and yields inconsistent diagnostic results among cytopathologists. Existing AI detection models perform poorly under real clinical conditions, primarily restricted by two key constraints: unbalanced spatial distribution of cell populations in TCT slides, and limited high-quality annotated cytology data relying on professional pathologist labeling. To address these limitations, we propose a Cell-Distribution Normalization (C-Norm) method. By decoupling abnormal and normal cells from the original TCT images and re-synthesizing them, this method ensures a uniform distribution of cell populations, thereby mitigating generalization degradation caused by distribution bias. Building upon this, we integrate the YOLOv12 framework with a DINOv3 module. This hybrid architecture leverages the advanced detection capability of YOLO models and the superior feature representations of DINOv3 to capture subtle morphological nuances essential for precise recognition of TCT images. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance, significantly outperforming mainstream detection algorithms. The complete implementation is available at: this https URL

Comments: 33;11

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.13116 [cs.CV]

(or arXiv:2607.13116v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.13116

arXiv-issued DOI via DataCite

Submission history

From: Dawei Dai [view email] [v1] Tue, 14 Jul 2026 14:53:04 UTC (77,951 KB)

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