no code implementations • 15 Sep 2023 • Kaouther Mouheb, Mobina Ghojogh Nejad, Lavsen Dahal, Ehsan Samei, W. Paul Segars, Joseph Y. Lo
In this study, we leverage recent advancements in geometric deep learning and denoising diffusion probabilistic models to refine the segmentation results of the large intestine.
no code implementations • 17 Aug 2023 • Fakrul Islam Tushar, Lavsen Dahal, Saman Sotoudeh-Paima, Ehsan Abadi, W. Paul Segars, Ehsan Samei, Joseph Y. Lo
Model performance on virtual CT and CXR images was comparable to overall results on clinical data.
no code implementations • 7 Mar 2022 • Fakrul Islam Tushar, Ehsan Abadi, Saman Sotoudeh-Paima, Rafael B. Fricks, Maciej A. Mazurowski, W. Paul Segars, Ehsan Samei, Joseph Y. Lo
However, performance dropped to an AUC of 0. 65 and 0. 69 when evaluated on clinical and our simulated CVIT-COVID dataset.
no code implementations • 3 Mar 2022 • Fakrul Islam Tushar, Husam Nujaim, Wanyi Fu, Ehsan Abadi, Maciej A. Mazurowski, Ehsan Samei, William P. Segars, Joseph Y. Lo
This demonstrates that quality data is the key to improving the model's performance.
no code implementations • 23 Feb 2022 • Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin, Ehsan Samei, Joseph Y. Lo
Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD).
no code implementations • 20 Aug 2020 • Wanyi Fu, Shobhit Sharma, Ehsan Abadi, Alexandros-Stavros Iliopoulos, Qi. Wang, Joseph Y. Lo, Xiaobai Sun, William P. Segars, Ehsan Samei
Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical images.
1 code implementation • 3 Aug 2020 • Fakrul Islam Tushar, Vincent M. D'Anniballe, Rui Hou, Maciej A. Mazurowski, Wanyi Fu, Ehsan Samei, Geoffrey D. Rubin, Joseph Y. Lo
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. Materials & Methods: This retrospective study included a total of 12, 092 patients (mean age 57 +- 18; 6, 172 women) for model development and testing (from 2012-2017).
no code implementations • 22 Jan 2020 • Rafael B. Fricks, Justin Solomon, Ehsan Samei
Imaging phantoms are test patterns used to measure image quality in computer tomography (CT) systems.