Cost-Effective Active Labeling for Data-Efficient Cervical Cell Classification
This work addresses the need for affordable and efficient labeling in cervical cancer diagnosis, offering an incremental improvement over existing methods by optimizing human cost usage.
The paper tackles the problem of high human labeling costs for training cervical cell classification models by proposing a cost-effective active labeling method that selects uncertain images for annotation, achieving data-efficient classification with reduced labeling effort.
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.