Automated Quality Evaluation of Cervical Cytopathology Whole Slide Images Based on Content Analysis
Journal:
arXiv
Published Date:
May 20, 2025
Abstract
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.
