no code implementations • 15 Dec 2022 • Engin Dikici, Xuan Nguyen, Noah Takacs, Luciano M. Prevedello
During the deployment, a given test data's LSM distribution is processed to detect its deviation from the forced distribution; hence, the AI system could predict its generalizability status for any previously unseen data set.
no code implementations • 10 Nov 2021 • Engin Dikici, Xuan V. Nguyen, Matthew Bigelow, John. L. Ryu, Luciano M. Prevedello
The framework utilizing only the labeled exams produced 9. 23 false positives for 90% BM detection sensitivity; whereas, the framework using the introduced learning strategy led to ~9% reduction in false detections (i. e., 8. 44) for the same sensitivity level.
1 code implementation • 5 Jul 2021 • Ujjwal Baid, Satyam Ghodasara, Suyash Mohan, Michel Bilello, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, Felipe C. Kitamura, Sarthak Pati, Luciano M. Prevedello, Jeffrey D. Rudie, Chiharu Sako, Russell T. Shinohara, Timothy Bergquist, Rong Chai, James Eddy, Julia Elliott, Walter Reade, Thomas Schaffter, Thomas Yu, Jiaxin Zheng, Ahmed W. Moawad, Luiz Otavio Coelho, Olivia McDonnell, Elka Miller, Fanny E. Moron, Mark C. Oswood, Robert Y. Shih, Loizos Siakallis, Yulia Bronstein, James R. Mason, Anthony F. Miller, Gagandeep Choudhary, Aanchal Agarwal, Cristina H. Besada, Jamal J. Derakhshan, Mariana C. Diogo, Daniel D. Do-Dai, Luciano Farage, John L. Go, Mohiuddin Hadi, Virginia B. Hill, Michael Iv, David Joyner, Christie Lincoln, Eyal Lotan, Asako Miyakoshi, Mariana Sanchez-Montano, Jaya Nath, Xuan V. Nguyen, Manal Nicolas-Jilwan, Johanna Ortiz Jimenez, Kerem Ozturk, Bojan D. Petrovic, Chintan Shah, Lubdha M. Shah, Manas Sharma, Onur Simsek, Achint K. Singh, Salil Soman, Volodymyr Statsevych, Brent D. Weinberg, Robert J. Young, Ichiro Ikuta, Amit K. Agarwal, Sword C. Cambron, Richard Silbergleit, Alexandru Dusoi, Alida A. Postma, Laurent Letourneau-Guillon, Gloria J. Guzman Perez-Carrillo, Atin Saha, Neetu Soni, Greg Zaharchuk, Vahe M. Zohrabian, Yingming Chen, Milos M. Cekic, Akm Rahman, Juan E. Small, Varun Sethi, Christos Davatzikos, John Mongan, Christopher Hess, Soonmee Cha, Javier Villanueva-Meyer, John B. Freymann, Justin S. Kirby, Benedikt Wiestler, Priscila Crivellaro, Rivka R. Colen, Aikaterini Kotrotsou, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Hassan Fathallah-Shaykh, Roland Wiest, Andras Jakab, Marc-Andre Weber, Abhishek Mahajan, Bjoern Menze, Adam E. Flanders, Spyridon Bakas
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society.
no code implementations • 27 May 2021 • Engin Dikici, Xuan V. Nguyen, Matthew Bigelow, Luciano M. Prevedello
In this study, we introduce a novel BM candidate detection CNN (cdCNN) to replace this classical IP stage.
no code implementations • 28 Sep 2020 • Vikash Gupta, Clayton Taylor, Sarah Bonnet, Luciano M. Prevedello, Jeffrey Hawley, Richard D. White, Mona G. Flores, Barbaros Selnur Erdal
In order to maximize the efficacy of breast cancer screening programs, proper mammographic positioning is paramount.
no code implementations • 10 Aug 2020 • Richard D. White, Barbaros S. Erdal, Mutlu Demirer, Vikash Gupta, Matthew T. Bigelow, Engin Dikici, Sema Candemir, Mauricio S. Galizia, Jessica L. Carpenter, Thomas P. O Donnell, Abdul H. Halabi, Luciano M. Prevedello
The two-phase approach consisted of (1) Phase 1 - focused on the development and preliminary testing of an algorithm for vessel-centerline extraction classification in a balanced study population (n = 500 with 50% disease prevalence) derived by retrospective random case selection; and (2) Phase 2 - concerned with simulated-clinical Trialing of the developed algorithm on a per-case basis in a more real-world study population (n = 100 with 28% disease prevalence) from an ED chest-pain series.
no code implementations • 24 Feb 2020 • Sema Candemir, Xuan V. Nguyen, Luciano M. Prevedello, Matthew T. Bigelow, Richard D. White, Barbaros S. Erdal
Purpose: This study investigates whether a machine-learning-based system can predict the rate of cognitive decline in mildly cognitively impaired patients by processing only the clinical and imaging data collected at the initial visit.
no code implementations • 26 Nov 2019 • Sema Candemir, Richard D. White, Mutlu Demirer, Vikash Gupta, Matthew T. Bigelow, Luciano M. Prevedello, Barbaros S. Erdal
We have evaluated the system on a reference dataset representing247 patients with atherosclerosis and 246 patients free of atherosclerosis.
no code implementations • 14 Aug 2019 • Barbaros S. Erdal, Mutlu Demirer, Chiemezie C. Amadi, Gehan F. M. Ibrahim, Thomas P. O'Donnell, Rainer Grimmer, Andreas Wimmer, Kevin J. Little, Vikash Gupta, Matthew T. Bigelow, Luciano M. Prevedello, Richard D. White
CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses).