HPMI: A retinal fundus image dataset for identification of high and pathological myopia based on deep learning

Myopia is one of the leading causes of visual impairment worldwide and can progress to high or pathological myopia (HM or PM) if proper measures were not taken. Accurate identification of HM and PH plays an important role for their intervention and treatment, which can be implemented leveraging deep learning technology on sufficient annotation image data. However, few efforts have been made to construct public accessible annotation data for this task. In this paper, we constructed a retinal fundus image dataset (called HPMI) to identify the HM and PM. This dataset consists of 4011 fundus images with their corresponding annotations for HM and PM, which were confirmed by multiple ophthalmic examinations (e.g., visual acuity and axial length). To the best of our knowledge, this is the largest fundus image dataset for the classification of HM and PM. Based on the dataset, we further validated the classification potential of three representative deep learning networks (i.e., ResNet50, DenseNet121, and InceptionV3) and analyzed the consistency between prediction results and annotations