Adapting Multi-view Correlation Learning for Enhanced Cervical Dysplasia Diagnosis

Analyzing relationships among pathological images under varying conditions during colposcopy is crucial for diagnosing cervical dysplasia. However, images from different conditions present challenges in accurate alignment and scaling, and the lesion features exhibit inconsistencies. These factors increase the difficulty of precisely capturing correlations between lesions across different views, especially without lesion annotations. To address these challenges, we propose the Dual Modalities for Multi-view Correlation Learning (D2MC) method. It enhances lesion representation by considering connections between text and visual data and relationships among multiple views. Specifically, the model first aligns lesion-specific text attributes with images at a fine-grained level, adaptively adjusting the visual model’s attention to focus on the lesion. It then incorporates a Temporal Correlation Adapter (TC-Adapter) into the encoder and introduces a Multi-View Fusion Module (MVFM) to strengthen multi-view correlation learning from a visual attention perspective. Finally, it aligns learnable text prompts with diagnostic outcomes and integrates a visual feature classifier to enhance diagnostic precision. Experimental results demonstrate that D2MC advances colposcopic image diagnosis by effectively capturing key correlations across different images.