  Cervical cancer is the fourth most common cancer in women worldwide. The
availability of a robust automated cervical image classification system can
augment the clinical care provider's limitation in traditional visual
inspection with acetic acid (VIA). However, there are a wide variety of
cervical inspection objectives which impact the labeling criteria for
criteria-specific prediction model development. Moreover, due to the lack of
confirmatory test results and inter-rater labeling variation, many images are
left unlabeled.
  Motivated by these challenges, we propose a self-supervised learning (SSL)
based approach to produce a pre-trained cervix model from unlabeled cervical
images. The developed model is further fine-tuned to produce criteria-specific
classification models with the available labeled images. We demonstrate the
effectiveness of the proposed approach using two cervical image datasets. Both
datasets are partially labeled and labeling criteria are different. The
experimental results show that the SSL-based initialization improves
classification performance (Accuracy: 2.5% min) and the inclusion of images
from both datasets during SSL further improves the performance (Accuracy: 1.5%
min). Further, considering data-sharing restrictions, we experimented with the
effectiveness of Federated SSL and find that it can improve performance over
the SSL model developed with just its images. This justifies the importance of
SSL-based cervix model development. We believe that the present research shows
a novel direction in developing criteria-specific custom deep models for
cervical image classification by combining images from different sources
unlabeled and/or labeled with varying criteria, and addressing image access
restrictions.
