用少人工标注实现精准宫颈细胞分类。
Cost-Effective Active Labeling for Data-Efficient Cervical Cell Classification
- 基于分类器不确定性筛选最需标注的细胞图像，降低标注成本。
- 仅用少量标注数据即构建出有代表性的训练集，提升分类效率。
- 适合资源有限但需高精度宫颈癌筛查的研究团队使用。
宫颈细胞的数量与类别信息对宫颈癌诊断至关重要。然而，现有自动分类方法需具有代表性的训练数据集，而构建此类数据集需高昂的人工标注成本。本文提出一种低成本主动标注方法，通过高效估算分类器对未标注宫颈细胞图像的不确定性，精准选择最具价值的图像进行标注，从而以更小的人力投入构建具备代表性的训练集。大量实证结果验证了该方法在提升数据代表性与控制人工成本方面的有效性，为实现数据高效宫颈细胞分类开辟新路径。
原文摘要 · Abstract (English)
Information on the number and category of cervical cells is crucial for the diagnosis of cervical cancer. However, existing classification methods capable of automatically measuring this information require the training dataset to be representative, which consumes an expensive or even unaffordable human cost. We herein propose active labeling that enables us to construct a representative training dataset using a much smaller human cost for data-efficient cervical cell classification. This cost-effective method efficiently leverages the classifier's uncertainty on the unlabeled cervical cell images to accurately select images that are most beneficial to label. With a fast estimation of the uncertainty, this new algorithm exhibits its validity and effectiveness in enhancing the representative ability of the constructed training dataset. The extensive empirical results confirm its efficacy again in navigating the usage of human cost, opening the avenue for data-efficient cervical cell classification.
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