Cleaning Highly Unbalanced Multisource Image Dataset for Quality Control in Cervical Precancer Screening - Xue, Zhiyun; Guo, Peng; Angara, Sandeep; Pal, Anabik; Jeronimo, Jose; Desai, Kanan T.; Ajenifuja, Olusegun K.; Adepiti, Clement A.; Sanjose, Silvia D.; Schiffman, Mark; Antani, Sameer (Recent Trends in Image Processing and Pattern Recognition, 2022)
Automated visual evaluation (AVE) of uterine cervix images is a deep learning algorithm that aims to improve cervical pre-cancer screening in low or medium resource regions (LMRR). Image quality control is an important pre-step in the development and use of AVE. In our work, we use data retrospectively collected from different sources/providers for analysis. In addition to good images, the datasets include low-quality images, green-filter images, and post Lugol’s iodine images. The latter two are uncommon in VIA (visual inspection with acetic acid) and should be removed along with low-quality images. In this paper, we apply and compare two state-of-the-art deep learning networks to filter out those two types of cervix images after cervix detection. One of the deep learning networks is DeepSAD, a semi-supervised anomaly detection network, while the other is ResNeSt, an improved variant of the ResNet classification network. Specifically, we study and evaluate the algorithms on a highly unbalanced large dataset consisting of four subsets from different geographic regions acquired with different imaging device types. We also examine the cross-dataset performance of the algorithms. Both networks can achieve high performance (accuracy above 97% and F1 score above 94%) on the test set.