Hierarchical pathology screening for cervical abnormality. (April 2021)

• Record Type: Journal Article
• Authors: Zhou, Ming; Zhang, Lichi; Du, Xiaping; Ouyang, Xi; Zhang, Xin; Shen, Qijia; Luo, Dong; Fan, Xiangshan; Wang, Qian
• Is Part Of: Computerized medical imaging and graphics. Volume 89(2021)
• Publication Date: 2021-04
• Subjects: Object detection -- Image classification -- Cervical smear screening -- TCT examination; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.101892
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: We propose a novel and hierarchical framework for automatic cervical smear screening aiming at the robust performance. Our framework can automatically find and locate "abnormal" cells from WSI images and alert pathologists. Our framework consists of three stages to progressively suppress the errors and guarantee the robustness. Abstract: Cervical smear screening is an imaging-based cancer detection tool, which is of pivotal importance for the early-stage diagnosis. A computer-aided screening system can automatically find out if the scanned whole-slide images (WSI) with cervical cells are classified as "abnormal" or "normal", and then alert pathologists. It can significantly reduce the workload for human experts, and is therefore highly demanded in clinical practice. Most of the screening methods are based on automatic cervical cell detection and classification, but the accuracy is generally limited due to the high variation of cell appearance and lacking context information from the surroundings. Here we propose a novel and hierarchical framework for automatic cervical smear screening aiming at the robust performance of case-level diagnosis and finding suspected "abnormal" cells. Our framework consists of three stages. We commence by extracting a large number of pathology images from the scanned WSIs, and implementing abnormal cell detection to each pathology image. Then, we feed the detected "abnormal" cells with corresponding confidence into our novel classification model for a comprehensive analysis of the extracted pathology images. Finally, we summarize the classification outputs of all extracted images, and determine the overall screening result for the target case. Experiments show that our three-stage hierarchical method can effectively suppress the errors from cell-level detection, and provide an effective and robust way for cervical abnormality screening.