A novel automation-assisted cervical cancer reading method based on convolutional neural network

Publication date: Available online 21 February 2020Source: Biocybernetics and Biomedical EngineeringAuthor(s): Yao Xiang, Wanxin Sun, Changli Pan, Meng Yan, Zhihua Yin, Yixiong LiangAbstractCervical cytology screening using Pap smear or liquid-based cytology is one of the most widely followed and accepted method. Automation-assisted screening based on cervical cytology has become a necessity due to the manual screening method operated by a visual analysis for cervical cell specimen under the microscope of the glass slide is usually labor-intensive and time-consuming. While automation-assisted reading system can improve efficiency, their performance often relies on the success of accurate cell segmentation and hand-craft feature extraction. This paper presents an efficient and totally segmentation-free method for automated cervical cell screening that utilizes modern object detector to directly detect cervical cells or clumps, without the design of specific hand-crafted feature. Specifically, we use the state-of-the-art CNN-based object detection methods, YOLOv3, as our baseline model. In order to improve the classification performance of hard examples which are four highly similar categories, we cascade an additional task-specific classifier. We also investigate the presence of unreliable annotations and coped with them by smoothing the distribution of noisy labels. We comprehensively evaluate our methods on our test set which is consisted of 1014 annotated cervical cell imag...
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research