Superpixel-Enhanced Quaternion Feature Fusion and Contextualization Graph Contrastive Learning for Cervical Cancer Diagnosis

Coarse-grained classification methods have demonstrated robust performance across various image classification tasks. However, in colposcopy classification, these methods often struggle to effectively capture subtle lesion features. Fine-grained methods address this by merging multi-layer features to locate regions with high discriminative power. Nevertheless, such approaches frequently overlook contextual relationships between features and lose original shape information. Additionally, the similarity between lesion and normal regions further exacerbates classification challenges. To address these limitations, we propose SQG-net, a novel fine-grained classification method for cervical cancer diagnosis. SQG-net incorporates three innovative modules. First, the Quaternion Superpixel Encoder (QSE) preserves lesion shape and color features through superpixel segmentation and quaternion convolution. Next, the Hierarchical Quaternion Feature Selection (HQFS) network identifies fine-grained discriminative features, enhancing subtle feature differentiation. Finally, a Graph Context Learning Module (GCLM) captures contextual relationships between features. Additionally, contrastive learning is utilized to improve feature space separation, enhancing classification accuracy. The method was evaluated on both a private cervical imaging dataset and a publicly available dataset. SQG-net achieved significant improvements in classification accuracy, recording 88.97% on the private dataset and 79.11% on the publicly available dataset, establishing new state-of-the-art performance in cervical cancer classification. The code will be released upon conference acceptance.