Calibration Matters: Prototype-Aware Diffusion for OCT Cervical Classification With Calibration

Abstract

Cervical optical coherence tomography (OCT) imaging serves as an effective diagnostic tool, and the development of deep learning classification models for OCT has the potential to enhance diagnosis. However, the complex imaging patterns of OCT data, significant noise, and the substantial domain gap from multi-center data result in high uncertainty and low accuracy in classification networks. To address these challenges, we propose a Multi-scale Prototype-Guided Diffusion learning method (MPGD), which is constructed with the Multi-scale Feature Condition (MFC) , Diffusion-based Classification Calibrator (DCC) , and Multi-scale Prototype Bank (MPB) modules. Specifically, MFC provides initial classification based on multi-scale features, DCC calibrates MFC's classification results through a diffusion model, and MPB refines DCC's visual guidance using prototypes obtained from clustering. Extensive experiments demonstrate that MPGD outperforms widely-used competitors for cervical OCT image classification, showing excellent generalization performance.