Patch-to-Sample Reasoning for Cervical Cancer Screening of Whole Slide Image

Abstract

Deep learning has been instrumental in improving the accuracy of cervical cancer screening using whole-slide images (WSIs) in recent years. Due to the complexity of the computer-aided screening task, the pipeline typically involves detecting “abnormal” cervical cells, and runs classification at the patch and sample levels, respectively. While the patch-level classification for normal or abnormal cells cannot be perfect, the errors may accumulate across individual patches and make the subsequent sample-level classification even more difficult. To address these issues, we propose a Patch-to-Sample (P2S) reasoning method to screen the cervical abnormality in this paper. We first improve the patch-level classifier by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hard patch mining</i> strategy, such that the classifier is not only more accurate but also more powerful to represent suspicious cells in local patches. Then, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">score embedding</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">token pooling</i> to a transformer network, which aggregates multiple patches and derives the diagnosis result at the sample level. Experiments show that our P2S method can more effectively utilize the key patches in individual samples, and thus outperforms existing methods.