MedImg: An Integrated Database for Public Medical Images

TL;DR

MedImg is a new online database that compiles thousands of public medical images across various imaging modalities and organs to support deep learning research.

Contribution

The novel contribution is the curation and integration of 105 diverse public medical image datasets into a unified, accessible database.

Findings

MedImg contains 1,995,671 images across 14 modalities and 13 organs.

The database is designed to improve accessibility and support generalization in deep learning medical image analysis.

It is available as an open-access platform at the provided URL.

Abstract

The advancements in deep learning algorithms for medical image analysis have garnered significant attention in recent years. While several studies have shown promising results, with models achieving or even surpassing human performance, translating these advancements into clinical practice is still accompanied by various challenges. A primary obstacle lies in the availability of large-scale, well-characterized datasets for validating the generalization of approaches. To address this challenge, we curated a diverse collection of medical image datasets from multiple public sources, containing 105 datasets and a total of 1,995,671 images. These images span 14 modalities, including X-ray, computed tomography, magnetic resonance imaging, optical coherence tomography, ultrasound, and endoscopy, and originate from 13 organs, such as the lung, brain, eye, and heart. Subsequently, we constructed an online database, MedImg, which incorporates and systematically organizes these medical images to facilitate data accessibility. MedImg serves as an intuitive and open-access platform for facilitating research in deep learning-based medical image analysis, accessible at https://www.cuilab.cn/medimg/.

Graphical Abstract

Figures