Search Results for author: Luciano M. Prevedello

Found 9 papers, 1 papers with code

Prediction of Model Generalizability for Unseen Data: Methodology and Case Study in Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI

no code implementations15 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.

Advancing Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI using Noisy Student-based Training

no code implementations10 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.

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

1 code implementation5 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.

Benchmarking Brain Tumor Segmentation +2

Augmented Networks for Faster Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI

no code implementations27 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.

Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis during CCTA Evaluation of Chest-Pain in the Emergency Department: Preparing an Application for Real-World Use

no code implementations10 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.

Predicting Rate of Cognitive Decline at Baseline Using a Deep Neural Network with Multidata Analysis

no code implementations24 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.

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