提出两阶段集成框架，自动分类宫颈涂片图像中的健康、异常及无效样本。
An Ensemble-Based Two-Step Framework for Classification of Pap Smear Cell Images
- 先用神经网络判别图像是否为无效样本，再分类健康/异常细胞
- 在PS3C挑战数据集上实现高准确率，有效区分四类细胞图像
- 适合需要自动化辅助诊断的临床场景和医学图像分析研究者
宫颈癌早期检测对改善患者预后、降低死亡率至关重要，需尽早识别癌前病变。因此，巴氏涂片筛查应用日益广泛，导致细胞学家工作量显著增加，亟需自动化工具协助。为此，2025年与ISBI联合举办了宫颈涂片细胞分类挑战赛（PS3C），旨在推动自动化图像分类技术的发展。本研究分析的图像分为四类：正常、异常、两者兼具以及无效图像（无法用于诊断）。本文提出一种两阶段集成方法：首先通过神经网络判断图像是否为无效图像；若非无效，则由第二阶段神经网络将其分类为含正常细胞、异常细胞或两者兼具的图像。
原文摘要 · Abstract (English)
Early detection of cervical cancer is crucial for improving patient outcomes and reducing mortality by identifying precancerous lesions as soon as possible. As a result, the use of pap smear screening has significantly increased, leading to a growing demand for automated tools that can assist cytologists managing their rising workload. To address this, the Pap Smear Cell Classification Challenge (PS3C) has been organized in association with ISBI in 2025. This project aims to promote the development of automated tools for pap smear images classification. The analyzed images are grouped into four categories: healthy, unhealthy, both, and rubbish images which are considered as unsuitable for diagnosis. In this work, we propose a two-stage ensemble approach: first, a neural network determines whether an image is rubbish or not. If not, a second neural network classifies the image as containing a healthy cell, an unhealthy cell, or both.
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