SAC-Net: Enhancing Spatiotemporal Aggregation in Cervical Histological Image Classification via Label-Efficient Weakly Supervised Learning

Cervical cancer is the fourth most common cancer in women and its subtyping requires examining histopathological slides or digital images, such as whole slide images (WSIs). However, manually inspecting WSIs with gigapixel sizes can be laborious and prone to errors for pathologists. To address this issue, computer-aided approaches based on weakly-supervised learning techniques have been proposed. These methods can predict disease types directly from WSIs and highlight diagnosis-relevant regions, which can help pathologists achieve faster and more accurate diagnoses. WSIs are divided into overlapping patches using a sliding window approach, and these patches are subsequently screened in a sequential zig-zag pattern to identify spatiotemporal dependencies. These dependencies are further analyzed to generate predictions at the WSI level. Therefore, effective patch feature learning and spatiotemporal aggregation are two key issues in the weakly-supervised WSI classification (WSWC) task. In this paper, we present a label-efficient WSWC method called spatiotemporal aggregation for cervical WSIs (SAC-Net), which jointly performs online feature extraction and feature aggregation to infer the WSI-level prediction in an end-to-end manner. The online feature extractor helps to learn cervical-cancer-specific features and obtain more accurate patch representations. The feature aggregator uses an online instance clustering method to learn proper weight parameters for each cluster, which generates the WSI embedding with enhanced spatiotemporal aggregation. SAC-Net is developed and evaluated on a public cervical WSI dataset (TissueNet) containing 1015 WSIs, which are also externally tested on three independent cervical WSI datasets. Our results demonstrate that SAC-Net achieves state-of-the-art classification performance and is robust. SAC-Net has the potential to be a useful tool for clinical cervical cancer detection.