  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.
