Zobrazeno 1 - 10
of 590
pro vyhledávání: '"GEVAERT, OLIVIER"'
Autor:
Zhan, Xianghao, Liu, Yuzhe, Cecchi, Nicholas J., Towns, Jessica, Callan, Ashlyn A., Gevaert, Olivier, Zeineh, Michael M., Camarillo, David B.
Objective: Head impact information including impact directions, speeds and force are important to study traumatic brain injury, design and evaluate protective gears. This study presents a deep learning model developed to accurately predict head impac
Externí odkaz:
http://arxiv.org/abs/2409.08177
Autor:
Loutfi, Mahdi Ait Lhaj, Podasca, Teodora Boblea, Zwanenburg, Alex, Upadhaya, Taman, Barrios, Jorge, Raleigh, David R., Chen, William C., Capaldi, Dante P. I., Zheng, Hong, Gevaert, Olivier, Wu, Jing, Silva, Alvin C., Zhang, Paul J., Bai, Harrison X., Seuntjens, Jan, Löck, Steffen, Richard, Patrick O., Morin, Olivier, Reinhold, Caroline, Lepage, Martin, Vallières, Martin
Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational requirements all underscore the need for an effective method to identify the smallest set of predictive feature
Externí odkaz:
http://arxiv.org/abs/2407.04888
Autor:
de la Fuente, Jesus, Serrano, Guillermo, Veleiro, Uxía, Casals, Mikel, Vera, Laura, Pizurica, Marija, Pineda-Lucena, Antonio, Ochoa, Idoia, Vicent, Silve, Gevaert, Olivier, Hernaez, Mikel
Drug-target interaction (DTI) prediction is a challenging, albeit essential task in drug repurposing. Learning on graph models have drawn special attention as they can significantly reduce drug repurposing costs and time commitment. However, many cur
Externí odkaz:
http://arxiv.org/abs/2311.12670
Autor:
Warner, Elisa, Lee, Joonsang, Hsu, William, Syeda-Mahmood, Tanveer, Kahn, Charles, Gevaert, Olivier, Rao, Arvind
Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML,
Externí odkaz:
http://arxiv.org/abs/2311.02332
Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI
Autor:
Abbasian, Mahyar, Khatibi, Elahe, Azimi, Iman, Oniani, David, Abad, Zahra Shakeri Hossein, Thieme, Alexander, Sriram, Ram, Yang, Zhongqi, Wang, Yanshan, Lin, Bryant, Gevaert, Olivier, Li, Li-Jia, Jain, Ramesh, Rahmani, Amir M.
Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably
Externí odkaz:
http://arxiv.org/abs/2309.12444
Accurately labeling biomedical data presents a challenge. Traditional semi-supervised learning methods often under-utilize available unlabeled data. To address this, we propose a novel reliability-based training data cleaning method employing inducti
Externí odkaz:
http://arxiv.org/abs/2309.07332
Autor:
Zhan, Xianghao, Sun, Jiawei, Liu, Yuzhe, Cecchi, Nicholas J., Flao, Enora Le, Gevaert, Olivier, Zeineh, Michael M., Camarillo, David B.
Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the lack of generalizability caused by distributional shift of dif
Externí odkaz:
http://arxiv.org/abs/2306.05255
Autor:
Zhan, Xianghao, Liu, Yuzhe, Cecchi, Nicholas J., Callan, Ashlyn A., Flao, Enora Le, Gevaert, Olivier, Zeineh, Michael M., Grant, Gerald A., Camarillo, David B.
Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body. Mouthguards are used to measure head kinematics during impacts in traumatic brain injury (TBI) studies, but deviations from reference kinematics ca
Externí odkaz:
http://arxiv.org/abs/2212.09832
Autor:
Daneshjou, Roxana, Vodrahalli, Kailas, Novoa, Roberto A, Jenkins, Melissa, Liang, Weixin, Rotemberg, Veronica, Ko, Justin, Swetter, Susan M, Bailey, Elizabeth E, Gevaert, Olivier, Mukherjee, Pritam, Phung, Michelle, Yekrang, Kiana, Fong, Bradley, Sahasrabudhe, Rachna, Allerup, Johan A. C., Okata-Karigane, Utako, Zou, James, Chiou, Albert
Access to dermatological care is a major issue, with an estimated 3 billion people lacking access to care globally. Artificial intelligence (AI) may aid in triaging skin diseases. However, most AI models have not been rigorously assessed on images of
Externí odkaz:
http://arxiv.org/abs/2203.08807
Drug-induced liver injury (DILI) describes the adverse effects of drugs that damage liver. Life-threatening results including liver failure or death were also reported in severe DILI cases. Therefore, DILI-related events are strictly monitored for al
Externí odkaz:
http://arxiv.org/abs/2203.11015