I am happy to share that our RadioGraphics paper went live today! This paper aims to provide the current summary of IDC, our active developments and some of the future plans:
Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics (2023). doi:10.1148/rg.230180
In the coming week several of the IDC team members (Ron Kikinis @rkikinis, Steve Pieper @pieper, David Clunie @dclunie and myself) will be at the Annual meeting of the Radiological Society of North America (RSNA). Please stop by our educational exhibit at the learning center (exhibit INEE-45) (if you want to schedule time to meet, you are welcome to send me a PM), and join the IDC Deep Learning Lab session on Monday at 10:30 AM Central.
We have some exciting updates that I will announce after the meeting, introducing new features to help make IDC even more powerful and easier to use. Stop by our booth at RSNA to learn about those before the announcement!