Schacherer, D. P., Herrmann, M. D., Clunie, D. A., Höfener, H., Clifford, W., Longabaugh, W. J. R., Pieper, S., Kikinis, R., Fedorov, A., & Homeyer, A. (2023). The NCI Imaging Data Commons as a platform for reproducible research in computational pathology. arXiv. https://doi.org/10.48550/ARXIV.2303.09354
Machine learning-based (ML) image analysis algorithms have shown great potential to support the work of pathologists and to enable the discovery of novel biomarkers from tissue patterns. However, making ML-based studies reproducible by other researchers is still a key challenge.
This paper explores how the capabilities of the IDC and cloud-based ML services can be used in combination to improve the reproducibility of ML-based studies in pathology. It also provides a general introduction and advice on how to use the IDC for computational pathology.