RadImageNet: Training AI Models With Radiologic vs. Photographic Images

Yang Yang, PhD, Zahi Fayad, PhD, Xueyan Mei, PhD, Timothy Deyer, PhD and colleagues from Icahn School of Medicine at Mount Sinai, University of Oklahoma, and Weill Cornell Medicine conducted a study to evaluate the performance of AI models pretrained on radiologic images compared to photographic images. They created a large-scale, diverse medical imaging dataset to generate CNNs trained only from radiologic images. This is a significant study because the researchers demonstrated that pretraining with radiologic images rather than photographic images may result in more effective transfer learning for radiology AI models. A paper detailing the study entitled RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning was published in RSNA Radiology AI on July 27, 2022. Within 10 days of publication, the paper has been downloaded over 1,000 times.