Image Quality Classification for Automated Visual Evaluation of Cervical Precancer

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.

Keywords: Automated Visual Evaluation; Ensemble Learning; Image Quality; Mislabel Identification; Uterine Cervix Image.

Fig. 1.

Examples of images in each quality category.

Fig.2.

The classification confusion matrix of the test set

Fig. 1.

Diagram of the proposed approach.

Fig. 2.

T-SNE plots of training set