Prior Activation Map Guided Cervical OCT Image Classification

Cervical cancer poses a severe threat to women’s health globally. As a non-invasive imaging modality, cervical optical coherence tomography (OCT) rapidly generates micrometer-resolution images from the cervix, comparable nearly to histopathology. However, the scarcity of high-quality labeled OCT images and the inevitable speckle noise impede deep-learning models from extracting discriminative features of high-risk lesion images. This study utilizes segmentation masks and bounding boxes to construct prior activation maps (PAMs) that encode pathologists’ diagnostic insights into different cervical disease categories in OCT images. These PAMs guide the classification model in producing reasonable class activation maps during training, enhancing interpretability and performance to meet gynecologists’ needs. Experiments using five-fold cross-validation demonstrate that the PAM-guided classification model boosts the classification of high-risk lesions on three datasets. Besides, our method enhances histopathology-based interpretability to assist gynecologists in analyzing cervical OCT images efficiently, advancing the integration of deep learning in clinical practice.