BioDash: A semantic web dashboard for drug development
Eric K. Neumann, Dennis Quan
PSB 2006
Advances in medical imaging techniques and devices has resulted in increased use of imaging in monitoring disease progression in patients. However, extracting decision-enabling information from the resulting longitudinal multi-modal image sets poses a challenge. Radiologists often have to manually identify and quantify certain regions of interest in the longitudinal image sets, which bear upon the patient's condition. As the number of patients increases, the number of longitudinal multi-modal images grows, and the manual annotation and quantification of pathological concepts quickly becomes impractical. In this paper we explore how minimal annotations provided by the user at a few time points can be effectively leveraged to automatically annotate data in the entire multi-modal longitudinal image sets. In particular, we investigate the required number of annotated images per time point and across time for obtaining reasonable results for the entire image set, and what multi-modal cues can help boost the overall annotation results. © 2009 IEEE.
Eric K. Neumann, Dennis Quan
PSB 2006
Wesam Alramadeen, Yu Ding, et al.
IISE Transactions on Healthcare Systems Engineering
Andreana Gomez, Sergio Gonzalez, et al.
Toxics
John M. Prager, Jennifer J. Liang, et al.
AMIA Joint Summits on Translational Science 2017