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

Yang Qianl; Liu Xiany; Dai Daw; Chen Jing; Shen Xiaoj; Fu Kaiw; Tang Ming; Zou Dongl

cs.CVJul 14, 2026

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

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

Organizations: Chongqing University Cancer Hospital, Chongqing, China · Chongqing University of Posts and Telecommunications, Chongqing, China · Shanghai First Maternity and Infant Hospital, Tongji University, Shanghai, China

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: https://github.com/ddw2AIGROUP2CQUPT/Cell-Norm

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Jul 11, 2026 · cs.CVJ/K move · Enter open · S save

Geometry-aware Gaussian Prior and Axial Attention for Cervical Cytology Image Classification

Yating Li, Cheng Ye, Nenan Lyu, Weidong Chen+1

School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China · School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China · Department of Gynecologic Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China

Accurate cervical cytology image classification is a key component of automated cervical cancer screening, where reliable recognition of normal, precancerous, and cancer-associated cellular patterns from Pap smear images can improve screening efficiency and diagnostic consistency. However, this task remains challenging because cervical cells exhibit complex morphology, subtle intra-class variations, and strong inter-class similarities. Existing convolution-based models capture local texture well but have limited ability to model long-range relationships, whereas attention-based models provide broader context but often lack explicit structural guidance. To address these limitations, we propose a geometry-aware classification framework for cervical cancer screening-oriented cytology image analysis, incorporating semantic abstraction and structural priors learned from pre-trained vision-language features. The method uses Gaussian expert modules to generate axis-wise priors from global semantic information, capturing structural regularities such as nuclear alignment and cellular spatial organization. These priors are embedded into an axial self-attention module to modulate similarity computation along horizontal and vertical directions, improving long-range dependency modeling and structure-sensitive feature interaction. Experiments on the Mendeley liquid-based cytology and SIPaKMeD datasets show that the proposed method achieves 99.48% accuracy on the former and 96.08% on the latter, with balanced gains in recall, precision, and overall classification performance. Visual analysis further shows that the learned priors highlight diagnostically relevant cellular regions, demonstrating the potential of the proposed framework as a screening-oriented decision-support tool for cervical cytology.

Non-Small Cell Lung Cancer Cross-Attention Geometry-Aware

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Abstract

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