Semi-Supervised Learning for Cervical Precancer Detection

Convolutional neural networks have become the paradigm of choice for medical image classification applications. Recent research results have demonstrated that deep learning can provide a promising solution for cervical precancer, which is the direct precursor to invasive cervical cancer. However, labeled large datasets are required to develop robust, reliable, and portable deep learning algorithms. This paper presents a study of semi-supervised learning with split-attention models for cervical precancer classification using data derived from two large studies conducted by the U.S. National Cancer Institute. In this work, we examine semi-supervised learning with the ResNeSt50 architecture and observe a significant boost in performance over transfer learning from pre-trained ImageNet weights. We also analyze the issue of specular reflections which is very common in cervical photographic images. Specular reflection occurs as bright spots saturated with white light from the illuminant that occurs due to the presence of moisture and can distract machine learning algorithms. We explore various augmentation techniques to solve specular reflection problems to improve the visual quality of results. As a result, our approach brings significant performance improvements (82.02% accuracy) with potential application in AI device-assisted decision-making.