Abstract
Image quality control is a critical element in the process of data collection and cleaning. Both manual and automated analyses alike are adversely impacted by bad quality data. There are several factors that can degrade image quality and, correspondingly, there are many approaches to mitigate their negative impact. In this paper, we address image quality control toward our goal of improving the performance of automated visual evaluation (AVE) for cervical precancer screening. Specifically, we report efforts made toward classifying images into four quality categories (“unusable”, “unsatisfactory”, “limited”, and “evaluable”) and improving the quality classification performance by automatically identifying mislabeled and overly ambiguous images. The proposed new deep learning ensemble framework is an integration of several networks that consists of three main components: cervix detection, mislabel identification, and quality classification. We evaluated our method using a large dataset that comprises 87,420 images obtained from 14,183 patients through several cervical cancer studies conducted by different providers using different imaging devices in different geographic regions worldwide. The proposed ensemble approach achieved higher performance than the baseline approaches.
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Acknowledgement
This research was supported by the Intramural Research Program of the National Library of Medicine (NLM) and the Intramural Research Program of the National Cancer Institute (NCI). Both NLM and NCI are part of the National Institutes of Health (NIH). The NET study was supported partly by Global Good. The authors also want to thank Farideh Almani at NCI for her help.
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Appendix
Appendix
Examples of images in each quality category.
The classification confusion matrix of the test set
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Xue, Z. et al. (2022). Image Quality Classification for Automated Visual Evaluation of Cervical Precancer. In: Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds) Medical Image Learning with Limited and Noisy Data. MILLanD 2022. Lecture Notes in Computer Science, vol 13559. Springer, Cham. https://doi.org/10.1007/978-3-031-16760-7_20
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DOI: https://doi.org/10.1007/978-3-031-16760-7_20
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