Zobrazeno 1 - 10
of 235
pro vyhledávání: '"Foran David J"'
Autor:
Idnay, Betina, Xu, Zihan, Adams, William G., Adibuzzaman, Mohammad, Anderson, Nicholas R., Bahroos, Neil, Bell, Douglas S., Bumgardner, Cody, Campion, Thomas, Castro, Mario, Cimino, James J., Cohen, I. Glenn, Dorr, David, Elkin, Peter L, Fan, Jungwei W., Ferris, Todd, Foran, David J., Hanauer, David, Hogarth, Mike, Huang, Kun, Kalpathy-Cramer, Jayashree, Kandpal, Manoj, Karnik, Niranjan S., Katoch, Avnish, Lai, Albert M., Lambert, Christophe G., Li, Lang, Lindsell, Christopher, Liu, Jinze, Lu, Zhiyong, Luo, Yuan, McGarvey, Peter, Mendonca, Eneida A., Mirhaji, Parsa, Murphy, Shawn, Osborne, John D., Paschalidis, Ioannis C., Harris, Paul A., Prior, Fred, Shaheen, Nicholas J., Shara, Nawar, Sim, Ida, Tachinardi, Umberto, Waitman, Lemuel R., Wright, Rosalind J., Zai, Adrian H., Zheng, Kai, Lee, Sandra Soo-Jin, Malin, Bradley A., Natarajan, Karthik, Price II, W. Nicholson, Zhang, Rui, Zhang, Yiye, Xu, Hua, Bian, Jiang, Weng, Chunhua, Peng, Yifan
This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) P
Externí odkaz:
http://arxiv.org/abs/2410.12793
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2023)
Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. To reduce intra- and inter-observer variability, during the radiological assessment, computer-aided diagnostic tools have been utilized to sup
Externí odkaz:
http://arxiv.org/abs/2304.12988
The role of chest X-ray (CXR) imaging, due to being more cost-effective, widely available, and having a faster acquisition time compared to CT, has evolved during the COVID-19 pandemic. To improve the diagnostic performance of CXR imaging a growing n
Externí odkaz:
http://arxiv.org/abs/2208.01843
Computed tomography (CT) and chest X-ray (CXR) have been the two dominant imaging modalities deployed for improved management of Coronavirus disease 2019 (COVID-19). Due to faster imaging, less radiation exposure, and being cost-effective CXR is pref
Externí odkaz:
http://arxiv.org/abs/2104.01617
Publikováno v:
International Journal of Computer Assisted Radiology and Surgery, 2021
Recently, the outbreak of the novel Coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. Due to limited availability of test kits, the need for auxiliary diagnostic approach has increased. Recent research has s
Externí odkaz:
http://arxiv.org/abs/2011.03585
Autor:
Cukierski William J, Nandy Kaustav, Gudla Prabhakar, Meaburn Karen J, Misteli Tom, Foran David J, Lockett Stephen J
Publikováno v:
BMC Bioinformatics, Vol 13, Iss 1, p 232 (2012)
Abstract Background Correct segmentation is critical to many applications within automated microscopy image analysis. Despite the availability of advanced segmentation algorithms, variations in cell morphology, sample preparation, and acquisition set
Externí odkaz:
https://doaj.org/article/fcebcd8e64d54446b017f74a857da0e1
Autor:
Ianosi-Irimie Monica, Hall Bonnie H, Javidian Parisa, Chen Wenjin, Ganesan Shridar, Foran David J
Publikováno v:
BMC Medical Imaging, Vol 8, Iss 1, p 11 (2008)
Abstract Background Breast cancers that overexpress the human epidermal growth factor receptor 2 (HER2) are eligible for effective biologically targeted therapies, such as trastuzumab. However, accurately determining HER2 overexpression, especially i
Externí odkaz:
https://doaj.org/article/44bc89946f7a455080de190cead2d8c1
Autor:
Foran, David J1 (AUTHOR) foran@cinj.rutgers.edu, Chen, Wenjin1 (AUTHOR), Kurc, Tahsin2 (AUTHOR), Gupta, Rajarshi2 (AUTHOR), Kaczmarzyk, Jakub Roman3 (AUTHOR), Torre-Healy, Luke Austin3 (AUTHOR), Bremer, Erich2 (AUTHOR), Ajjarapu, Samuel4 (AUTHOR), Do, Nhan4 (AUTHOR), Harris, Gerald5 (AUTHOR), Stroup, Antoinette5 (AUTHOR), Durbin, Eric6 (AUTHOR), Saltz, Joel H2 (AUTHOR)
Publikováno v:
Cancer Informatics. 2/4/2024, p1-6. 6p.
Designing the structure of neural networks is considered one of the most challenging tasks in deep learning, especially when there is few prior knowledge about the task domain. In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approa
Externí odkaz:
http://arxiv.org/abs/1806.01940
In this paper, we propose Factorized Adversarial Networks (FAN) to solve unsupervised domain adaptation problems for image classification tasks. Our networks map the data distribution into a latent feature space, which is factorized into a domain-spe
Externí odkaz:
http://arxiv.org/abs/1806.01376