Robust whole slide image analysis for cervical cancer screening using deep learning

Computer-assisted diagnosis is key for popularizing cervical cancer screening. However, current recognition algorithms are insufficient in accuracy and generalization for cervical lesion cells, especially when facing diversity data in clinical applications. Inspired by manual reading slide under microscopes, we develop a progressive lesion cell recognition method combing low and high resolutions WSIs to recommend lesion cells and a recurrent neural network-based WSI classification model to evaluate the lesion degree of WSIs. After validating our system on 3,545 patient-wise WSIs with 79,218 annotations from multiple hospitals and several imaging instruments, on multi-center independent test sets of 1,170 patient-wise WSIs, we achieve 93.5% Specificity and 95.1% Sensitivity for classifying slides, closely equivalent to the average level of three independent cytopathologists, and obtain 88.5% TPR (true positive rate) for recommending top 10 lesion cells on 447 positive slides. After deploying, our system recognizes one giga-pixel WSI in about 1.5 minutes using one Nvidia 1080Ti GPU.