MVDI25K: A large-scale dataset of microscopic vaginal discharge images

Abstract

With the widespread application of artificial intelligence technology in the field of biomedical images, the deep learning-based detection of vaginal discharge, an important but challenging topic in medical image processing, has drawn an increasing amount of research interest. Although the past few decades have witnessed major advances in object detection of natural scenes, such successes have been slow to medical images, not only because of the complex background and diverse cell morphology in the microscope images, but also due to the scarcity of well-annotated datasets of objects in medical images. Until now, in most hospitals in China, the vaginal diseases are often checked by observation of cell morphology using the microscope manually, or observation of the color reaction experiment by inspectors, which are time-consuming, inefficient and easily interfered by subjective factors. To this end, we elaborately construct the first large-scale dataset of microscopic vaginal discharge images, named MVDI25K, which consists of 25,708 images covering 10 cell categories related to vaginal discharge detection. All the images in MVDI25K dataset are carefully annotated by experts with bounding-box and object-level labels. In addition, we conduct a systematical benchmark experiments on MVDI25K dataset with 10 representative state-of-the-art (SOTA) deep models focusing on two key tasks, i.e., object detection and object segmentation. Our research offers the community an opportunity to explore more in this new field.