Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets

TL;DR

This paper introduces a self-supervised learning approach to develop a robust cervical image classification model from heterogeneous, partially labeled datasets, enhancing accuracy and addressing data sharing restrictions.

Contribution

It presents a novel SSL-based method for pre-training and fine-tuning cervical image models across diverse datasets with different labeling criteria and limited labels.

Findings

SSL initialization improves classification accuracy by at least 2.5%.

Including data from multiple datasets further enhances performance.

Federated SSL can outperform models trained on individual datasets.

Abstract

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.