Center-Aware Detection with Swin-based Co-DETR Framework for Cervical Cytology
School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）
Yan Kong, Yuan Yin, Hongan Chen, Yuqi Fang, Caifeng Shan
备注：
ISBI 2026Accepted Paper & Winning Solution for the RIVA Cervical Cytology Challenge
Pap smear图像的自动化分析对宫颈癌筛查至关重要，但因细胞分布密集和形态复杂而具有挑战性。本文提出我们的RIVA宫颈涂片挑战赛获奖方案，在Track B获得第一名，在Track A获得第二名。我们的方法结合了Co-DINO框架与Swin-Large主干网络，以实现稳健的多尺度特征提取。为解决数据集的固定大小边界框标注问题，我们将检测任务转化为中心点预测问题。针对此形式，我们引入了中心保持的数据增强策略和解析几何框优化，以有效吸收定位抖动。最后，我们应用了针对跟踪的损失调节，以适应每个任务的损失权重。实验表明，我们的针对性优化提高了检测性能，为宫颈涂片图像分析提供了一个有效的流程。我们的代码可在https://github.com/YanKong0408/Center-DETR获取。
英文摘要：
Automated analysis of Pap smear images is critical for cervical cancer screening but remains challenging due to dense cell distribution and complex morphology. In this paper, we present our winning solution for the RIVA Cervical Cytology Challenge, achieving 1st place in Track B and 2nd place in Track A. Our approach leverages a powerful baseline, integrating the Co-DINO framework with a Swin-Large backbone for robust multi-scale feature extraction. To address the dataset's unique fixed-size bounding box annotations, we formulate the detection task as a center-point prediction problem. Tailoring our approach to this formulation, we introduce a center-preserving data augmentation strategy and an analytical geometric box optimization to effectively absorb localization jitter. Finally, we apply track-specific loss tuning to adapt the loss weights for each task. Experiments demonstrate that our targeted optimizations improve detection performance, providing an effective pipeline for cytology image analysis. Our code is available at https://github.com/YanKong0408/Center-DETR.
University of Southern Maine（南缅因大学）；University of South Florida（南佛罗里达大学）；SRC Biosciences（SRC生物科学公司）
Analyzing Pap cytology slides is an important tasks in detecting and grading precancerous and cancerous cervical cancer stages. Processing cytology images usually involve segmenting nuclei and overlapping cells. We introduce a cervical cytology dataset that can be used to evaluate nucleus detection, as well as image classification methods in the cytology image processing area. This dataset contains 93 real image stacks with their grade labels and manually annotated nuclei within images. We also present two methods: a baseline method based on a previously proposed approach, and a deep learning …
Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation
The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；Tencent AI Lab（腾讯AI实验室）；The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院）；Peking University Shenzhen Hospital（北京大学深圳医院）；Shenzhen PKU-HKUST Medical Center（深圳北医港科大医疗中心）；Shenzhen Key Laboratory on Technology for Early Diagnosis of Major Gynecologic Diseases（深圳市重大妇科疾病早期诊断技术重点实验室）；Harvard University（哈佛大学）
Jadavpur University（贾达普大学）；Theism Medical Diagnostics Centre（Theism医学诊断中心）
Cervical cancer is the fourth most common category of cancer, affecting more than 500,000 women annually, owing to the slow detection procedure. Early diagnosis can help in treating and even curing cancer, but the tedious, time-consuming testing process makes it impossible to conduct population-wise screening. To aid the pathologists in efficient and reliable detection, in this paper, we propose a fully automated computer-aided diagnosis tool for classifying single-cell and slide images of cervical cancer. The main concern in developing an automatic detection tool for biomedical image classifi…
Missouri University of Science and Technology（密苏里科技大学）；Lister Hill National Center for Biomedical Communications（利斯特希尔生物医学通信中心）；U.S. National Library of Medicine（美国国家医学图书馆）；Diagnostic Systems Women’s Health and Cancer（诊断系统女性健康与癌症部门）；Becton Dickinson and Company（碧迪公司）
Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for collecting a majority of the cervical cells and aiding cytopathologists in locating abnormal cells. Most methods published in the research literature rely on accurate cell segmentation as a prior, which remains challenging due to a variety of factors, e.g., stain consistency, presence of clustered cells, etc. We propose a method for automatic classification of cervical slide images through generation of labeled cervical patch data and extracting dee…
Holistic and Historical Instance Comparison for Cervical Cell Detection
The Hong Kong University of Science and Technology（香港科技大学）；Tencent AI Lab（腾讯AI实验室）；Imsight AI Research Lab（依图AI研究院）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳香港协同创新研究院）
Jadavpur University（贾达普大学）；Theism Medical Diagnostics Centre（Theism医学诊断中心）
Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes Deep Learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts Deep feature from several Convolution Neural Network models and uses a two-step feature reduction approac…
Jadavpur University（贾达普大学）；Theism Medical Diagnostics Centre（Theism医学诊断中心）
Cytology is the branch of pathology which deals with the microscopic examination of cells for diagnosis of carcinoma or inflammatory conditions. Automation in cytology started in the early 1950s with the aim to reduce manual efforts in diagnosis of cancer. The inflush of intelligent technological units with high computational power and improved specimen collection techniques helped to achieve its technological heights. In the present survey, we focus on such image processing techniques which put steps forward towards the automation of cytology. We take a short tour to 17 types of cytology and …
Shenzhen University（深圳大学）；Shenzhen Institute of Artificial Intelligence of Robotics of Society（深圳市人工智能与机器人研究院）；University of California, Irvine（加州大学欧文分校）
Due to the difficulty of cancer samples collection and annotation, cervical cancer datasets usually exhibit a long-tailed data distribution. When training a detector to detect the cancer cells in a WSI (Whole Slice Image) image captured from the TCT (Thinprep Cytology Test) specimen, head categories (e.g. normal cells and inflammatory cells) typically have a much larger number of samples than tail categories (e.g. cancer cells). Most existing state-of-the-art long-tailed learning methods in object detection focus on category distribution statistics to solve the problem in the long-tailed scena…
Huazhong University of Science and Technology（华中科技大学）；Maternal and Child Hospital of Hubei Province（湖北省妇幼保健院）；Tongji Hospital（同济医院）
Digital gigapixel whole slide image (WSI) is widely used in clinical diagnosis, and automated WSI analysis is key for computer-aided diagnosis. Currently, analyzing the integrated descriptor of probabilities or feature maps from massive local patches encoded by ResNet classifier is the main manner for WSI-level prediction. Feature representations of the sparse and tiny lesion cells in cervical slides, however, are still challenging, while the unused location representations are available to supply the semantics classification. This study designs a novel and efficient framework with a new modul…
C-Norm: Cell-Distribution Normalization Enables Precision Recognition of Medical-Cell Image
Chongqing University Cancer Hospital（重庆大学附属肿瘤医院）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；Shanghai First Maternity and Infant Hospital, Tongji University（同济大学附属第一妇婴保健院）
