VMambaDA: Visual State Space Model with Unsupervised Domain Adaptation in Cervical Cancer

Cervical cancer remains a major public health concern in Vietnam, ranking second only to breast cancer among women. Early detection through screening, particularly HPV testing and Pap smears, is critical in reducing cervical cancer mortality. While deep learning has shown great promise in medical image analysis, particularly for detecting cervical cancer, challenges remain, especially when models are applied to real-world datasets with limited labeled data due to privacy concerns and expensive annotations. This research offers an effective cervical cancer diagnostic approach that combines Sliced Wasserstein Distance (SWD), Maximum Classifier Discrepancy (MCD), and the VMamba model. The VMambaDA model learns domain-invariant features and adjusts to domain shifts to address the problems of accuracy, generalization, and speed in cervical cancer screening. VMambaDA has proven to be more adept at handling the intricacies of medical images than earlier models by exhibiting better classification accuracy and sensitivity through extensive testing on both public and private datasets. This automated method could enhance the early detection of cervical cancer and intervention by pushing the limits of domain adaptation in cytopathology.