Automated Quality Evaluation of Cervical Cytopathology Whole Slide Images Based on Content Analysis
This addresses the need for automated quality assessment in cervical cancer screening to improve reliability and reduce costs, but it is incremental as it builds on existing computer-aided diagnosis methods.
The paper tackles the problem of subjective and unreliable manual quality evaluation of cervical cytopathology whole slide images by proposing an automated method based on AI algorithms and clinical data, achieving significant advantages in speed and consistency on 100 WSIs.
The ThinPrep Cytologic Test (TCT) is the most widely used method for cervical cancer screening, and the sample quality directly impacts the accuracy of the diagnosis. Traditional manual evaluation methods rely on the observation of pathologist under microscopes. These methods exhibit high subjectivity, high cost, long duration, and low reliability. With the development of computer-aided diagnosis (CAD), an automated quality assessment system that performs at the level of a professional pathologist is necessary. To address this need, we propose a fully automated quality assessment method for Cervical Cytopathology Whole Slide Images (WSIs) based on The Bethesda System (TBS) diagnostic standards, artificial intelligence algorithms, and the characteristics of clinical data. The method analysis the context of WSIs to quantify quality evaluation metrics which are focused by TBS such as staining quality, cell counts and cell mass proportion through multiple models including object detection, classification and segmentation. Subsequently, the XGBoost model is used to mine the attention paid by pathologists to different quality evaluation metrics when evaluating samples, thereby obtaining a comprehensive WSI sample score calculation model. Experimental results on 100 WSIs demonstrate that the proposed evaluation method has significant advantages in terms of speed and consistency.