Abstract
Insufficient coverage of cervical cytology screening in resource-limited areas remains a major bottleneck for women's health, as traditional centralized methods require significant investment and many qualified pathologists. Using consumer-grade electronic hardware and aspherical lenses, we design an ultra-low-cost and compact microscope. Given the microscope's low resolution, which hinders accurate identification of lesion cells in cervical samples, we train a coarse instance classifier to screen and extract feature sequences of the top 200 instances containing potential lesions from a slide. We further develop Att-Transformer to focus on and integrate the sparse lesion information from these sequences, enabling slide grading. Our model is trained and validated using 3510 low-resolution slides from female patients at four hospitals, and subsequently evaluated on four independent datasets. The system achieves area under the receiver operating characteristic curve values of 0.87 and 0.89 for detecting squamous intraepithelial lesions on 364 slides from female patients at two external primary hospitals, 0.89 on 391 newly collected slides from female patients at the original four hospitals, and 0.85 on 570 human papillomavirus positive slides from female patients. These findings demonstrate the feasibility of our AI-assisted approach for effective detection of high-risk cervical precancer among women in resource-limited regions.