Zobrazeno 1 - 10
of 1 065
pro vyhledávání: '"Giger, Maryellen"'
Autor:
Bajcsy, Peter, Bhattiprolu, Sreenivas, Boerner, Katy, Cimini, Beth A, Collinson, Lucy, Ellenberg, Jan, Fiolka, Reto, Giger, Maryellen, Goscinski, Wojtek, Hartley, Matthew, Hotaling, Nathan, Horwitz, Rick, Jug, Florian, Kreshuk, Anna, Lundberg, Emma, Mathur, Aastha, Narayan, Kedar, Onami, Shuichi, Plant, Anne L., Prior, Fred, Swedlow, Jason, Taylor, Adam, Keppler, Antje
Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well
Externí odkaz:
http://arxiv.org/abs/2401.13023
Autor:
Hoang, Trung-Hieu, Fuhrman, Jordan, Madduri, Ravi, Li, Miao, Chaturvedi, Pranshu, Li, Zilinghan, Kim, Kibaek, Ryu, Minseok, Chard, Ryan, Huerta, E. A., Giger, Maryellen
Facilitating large-scale, cross-institutional collaboration in biomedical machine learning projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is
Externí odkaz:
http://arxiv.org/abs/2312.08701
Autor:
Li, Zilinghan, He, Shilan, Chaturvedi, Pranshu, Hoang, Trung-Hieu, Ryu, Minseok, Huerta, E. A., Kindratenko, Volodymyr, Fuhrman, Jordan, Giger, Maryellen, Chard, Ryan, Kim, Kibaek, Madduri, Ravi
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the a
Externí odkaz:
http://arxiv.org/abs/2308.08786
Autor:
Whitney, Heather M., Baughan, Natalie, Myers, Kyle J., Drukker, Karen, Gichoya, Judy, Bower, Brad, Chen, Weijie, Gruszauskas, Nicholas, Kalpathy-Cramer, Jayashree, Koyejo, Sanmi, Sá, Rui C., Sahiner, Berkman, Zhang, Zi, Giger, Maryellen L.
Purpose: The Medical Imaging and Data Resource Center (MIDRC) open data commons was launched to accelerate the development of artificial intelligence (AI) algorithms to help address the COVID-19 pandemic. The purpose of this study was to quantify lon
Externí odkaz:
http://arxiv.org/abs/2303.10501
Autor:
Grossman, Robert L., Giger, Maryellen L., Johnson, Julie A., Marks, Jeremy D., Ridgway, Jessica P., Solway, Julian, Stadler, Walter M.
Academic medical centers are generating an increasing amount of biomedical data and there is an increasing demand for biomedical data for research purposes by research projects, research consortia, companies, and other third parties. At the same time
Externí odkaz:
http://arxiv.org/abs/2302.02425
Deep transfer learning using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown strong predictive power in characterization of breast lesions. However, pretrained convolutional neural networks (CNNs) require 2D inputs, limiting
Externí odkaz:
http://arxiv.org/abs/1911.03022
Autor:
Chen, Weijie, Sá, Rui C., Bai, Yuntong, Napel, Sandy, Gevaert, Olivier, Lauderdale, Diane S., Giger, Maryellen L.
Publikováno v:
In Heliyon July 2023 9(7)
Autor:
Conn Busch, Julian M., Cozzi, Joseph L., Li, Hui, Lan, Li, Giger, Maryellen L., Keutgen, Xavier M.
Publikováno v:
In Health Sciences Review June 2023 7
Autor:
Obuchowski, Nancy A, Huang, Erich, deSouza, Nandita M, Raunig, David, Delfino, Jana, Buckler, Andrew, Hatt, Charles, Wang, Xiaofeng, Moskowitz, Chaya, Guimaraes, Alexander, Giger, Maryellen, Hall, Timothy J, Kinahan, Paul, Pennello, Gene
Publikováno v:
In Academic Radiology February 2023 30(2):147-158
Akademický článek
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