Since 2014, the NCI has launched a series of data commons as part of the Cancer Research
Data Commons (CRDC) ecosystem housing genomic, proteomic, imaging, and clinical data …

Purpose: To develop an open-source and easy-to-use segmentation model that can
automatically and robustly segment most major anatomical structures in MR images …

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a
significant portion of the published literature lacks transparency and reproducibility, which …

Recent progress in deep learning (DL) is producing a new generation of tools across
numerous clinical applications. Within the analysis of brain tumors in magnetic resonance …

Background Since the introduction of TotalSegmentator CT, there has been demand for a
similar robust automated MRI segmentation tool that can be applied across all MRI …

Public imaging datasets are critical for the development and evaluation of automated tools in
cancer imaging. Unfortunately, many do not include annotations or image-derived features …

Purpose Federated training is often challenging on heterogeneous datasets due to
divergent data storage options, inconsistent naming schemes, varied annotation …

Since 2016 the Healthcare Information and Management Systems Society (HIMSS) and the
Society for Imaging Informatics in Medicine (SIIM) have collaborated to generate a series of …

Rapid advances in medical imaging Artificial Intelligence (AI) offer unprecedented
opportunities for automatic analysis and extraction of data from large imaging collections …

In recent years, medicine has undergone profound changes, strongly entering a new phase
defined as the “era of precision medicine”. In this context, patient clinical management …