Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis

TL;DR

This paper introduces STRIDE, an AI system for large-scale cervical precancerous screening that combines extensive data, interpretability, and robustness to domain shifts, significantly improving diagnostic accuracy and trustworthiness.

Contribution

STRIDE is a novel AI approach that integrates patient and cell-level labels, employs adversarial training for robustness, and generates interpretable explanations, advancing automated cervical cytology diagnosis.

Findings

Outperforms previous state-of-the-art methods in accuracy.

Demonstrates robustness across 183 medical centers.

Handles extensive datasets with high interpretability.

Abstract

Cervical Cancer continues to be the leading gynecological malignancy, posing a persistent threat to women's health on a global scale. Early screening via cytology Whole Slide Image (WSI) diagnosis is critical to prevent this Cancer progression and improve survival rate, but pathologist's single test suffers inevitable false negative due to the immense number of cells that need to be reviewed within a WSI. Though computer-aided automated diagnostic models can serve as strong complement for pathologists, their effectiveness is hampered by the paucity of extensive and detailed annotations, coupled with the limited interpretability and robustness. These factors significantly hinder their practical applicability and reliability in clinical settings. To tackle these challenges, we develop an AI approach, which is a Scalable Technology for Robust and Interpretable Diagnosis built on Extensive data (STRIDE) of cervical cytology. STRIDE addresses the bottleneck of limited annotations by integrating patient-level labels with a small portion of cell-level labels through an end-to-end training strategy, facilitating scalable learning across extensive datasets. To further improve the robustness to real-world domain shifts of cytology slide-making and imaging, STRIDE employs color adversarial samples training that mimic staining and imaging variations. Lastly, to achieve pathologist-level interpretability for the trustworthiness in clinical settings, STRIDE can generate explanatory textual descriptions that simulates pathologists' diagnostic processes by cell image feature and textual description alignment. Conducting extensive experiments and evaluations in 183 medical centers with a dataset of 341,889 WSIs and 0.1 billion cells from cervical cytology patients, STRIDE has demonstrated a remarkable superiority over previous state-of-the-art techniques.