Deep convolutional neural networks using an active learning strategy for cervical cancer screening and diagnosis - Li, Xueguang; Du, Mingyue; Zuo, Shanru; Zhou, Mingqing; Peng, Qiyao; Chen, Ziyao; Zhou, Junhua; He, Quanyuan (Frontiers in Bioinformatics, 2023)
Cervical cancer (CC) is the fourth most common malignant tumor among women worldwide. Constructing a high-accuracy deep convolutional neural networks (DCNN) for cervical cancer screening and diagnosis is important for successful prevention of cervical cancer. In this work, we proposed a robust deep convolutional neural network for cervical cancer screening using the whole slide images (WSI) of thinprep cytology test (TCT) slides from 211 cervical cancer and 189 normal patients. We used an active learning strategy to improve the efficiency and accuracy of image labelling. The sensitivity, specificity, and accuracy of the best model were 96.21%, 98.95% and 97.5%for CC patient identification respectively. Our results also demonstrated that the active learning strategy was superior to the traditional supervised learning strategy in cost reduction and improvement of the quality of image labelling. The related data and source code are free available at https://github.com/hqyone/cancer_rcnn