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 h...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/.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62025102,32301239,and 81921001)the Scientific and Technological Research Project of Xinjiang Production and Construction Corps(Grant No.2021AB028)the China Postdoctoral Science Foundation(Grant No.2023M740151).
文摘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/.
