MedImg: a database for public medical image integration

Abstract

The advancements of deep learning algorithms in medical image analysis has garnered tremendous attention in recent years. Several studies have reported that the models have achieved and even surpassed human performance, whereas the translation of these models into clinical practice is still accompanied by various challenges. A major challenge is the large-scale and well characterized dataset to validate the generalization of models. Therefore, we collected diverse medical image datasets from multiple public sources containing 103 datasets, 1,622,956 images. These images are derived from 14 modalities like XR, CT, MRI, OCT, ultrasound, and endoscopy, and from 9 organs such as lung, brain, eye, and heart. Subsequently, we constructed an online database, MedImg, which incorporates and hierarchically organizes medical images to facilitate data access. MedImg serves as an intuitive and open-access platform for contributing to deep learning-based medical image analysis, accessible at https://www.cuilab.cn/medimg/.