A Dual-Path Multiple Instance Learning Network Guided by Image Quality Assessment for Cervical Whole Slide Image Classification

Abstract

The existing cervical whole slide image classification methods ignore the influence of image quality, resulting in low classification accuracy. To address this, we propose a dual-path multiple instance learning classification method guided by image quality assessment. Specifically, a pre-trained quality assessment model assigns quality scores to patches, splitting them into high- and low-quality paths. In the high-quality path, patch features are weighted by their quality scores to emphasize reliable diagnostic regions. In the low-quality path, a key instance is selected using clustering and feature distance matching. Finally, a cross-attention module fuses features across quality levels. Our method achieves 94.64% accuracy and 91.74% AUC on a dataset of 2,434 WSIs collected from five medical centers, outperforming state-of-the-art methods.