Enhancing Model Generalization of Cervical Fluid-Based Cell Detection through Causal Feature Extraction:A Novel Method
Abstract
Cervical cancer is the most common gynecologic malignancy, and in clinical practice, cervical cancer is best treated if it is detected at an early stage. Thinprep Cytologic Test (TCT) is the best early detection method for cervical cancer as determined by the WHO. As the coverage of early detection of cervical cancer increases, the number of samples in hospitals increases annually, and the pressure on the pathologists to read the cytological images increases, which easily leads to an increase in the rate of misdiagnosis and missed diagnosis. Therefore, automatic detection of abnormal cells in cervical cytology images of cervical fluid using deep learning techniques has become a hot research topic today. However, existing deep learning models for cell detection often collect a single data source from a medical institution for construction. Different medical institutions have different equipment and staining methods, and the accuracy, magnification, and staining results of the images obtained will be different. As a result, the application performance of the model in different medical institution data is not good, and there is a problem of domain shift. To address these problems, this paper proposes a method for cervical fluid-based cell detection based on causal feature extraction. The method is based on the one-stage detection model RetinaNet, and incorporates causal autoencoder to learn the invariant causal feature representation from data. It reduces the impact of task-irrelevant feature representations, reduces the variability of feature distributions in different datasets, and effectively solves the domain shift problem. The addition of deformable convolution and attention mechanism enhances the feature extraction capability for foreground categories with variable shapes in cervical fluid-based pathology images. This reduces the impact of possible strong correlation between background features and goal cells, and reduces the interference of the foreground catego