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pro vyhledávání: '"Pickhardt, P"'
Autor:
Hou, Benjamin, Lee, Sung-Won, Lee, Jung-Min, Koh, Christopher, Xiao, Jing, Pickhardt, Perry J., Summers, Ronald M.
Purpose: To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian cancer. Materials and Methods: This retrospective study included contr
Externí odkaz:
http://arxiv.org/abs/2406.15979
Autor:
Koehler, Sven, Hussain, Tarique, Hussain, Hamza, Young, Daniel, Sarikouch, Samir, Pickhardt, Thomas, Greil, Gerald, Engelhardt, Sandy
Cardiac magnetic resonance (CMR) sequences visualise the cardiac function voxel-wise over time. Simultaneously, deep learning-based deformable image registration is able to estimate discrete vector fields which warp one time step of a CMR sequence to
Externí odkaz:
http://arxiv.org/abs/2209.05778
Autor:
Cao, Weiguo, Pomeroy, Marc J., Liang, Zhengrong, Gao, Yongfeng, Shi, Yongyi, Tan, Jiaxing, Han, Fangfang, Wang, Jing, Ma, Jianhua, Lu, Hongbin, Abbasi, Almas F., Pickhardt, Perry J.
The elasticity of soft tissues has been widely considered as a characteristic property to differentiate between healthy and vicious tissues and, therefore, motivated several elasticity imaging modalities, such as Ultrasound Elastography, Magnetic Res
Externí odkaz:
http://arxiv.org/abs/2205.14029
Autor:
Pickhardt, Rene, Richter, Stefan
Today, payment paths in Bitcoin's Lightning Network are found by searching for shortest paths on the fee graph. We enhance this approach in two dimensions. Firstly, we take into account the probability of a payment actually being possible due to the
Externí odkaz:
http://arxiv.org/abs/2107.05322
The Lightning Network (LN) is a prominent payment channel network aimed at addressing Bitcoin's scalability issues. Due to the privacy of channel balances, senders cannot reliably choose sufficiently liquid payment paths and resort to a trial-and-err
Externí odkaz:
http://arxiv.org/abs/2103.08576
Autor:
Ayis Pyrros, Stephen M. Borstelmann, Ramana Mantravadi, Zachary Zaiman, Kaesha Thomas, Brandon Price, Eugene Greenstein, Nasir Siddiqui, Melinda Willis, Ihar Shulhan, John Hines-Shah, Jeanne M. Horowitz, Paul Nikolaidis, Matthew P. Lungren, Jorge Mario Rodríguez-Fernández, Judy Wawira Gichoya, Sanmi Koyejo, Adam E Flanders, Nishith Khandwala, Amit Gupta, John W. Garrett, Joseph Paul Cohen, Brian T. Layden, Perry J. Pickhardt, William Galanter
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/e2d1fcc1da9a4a75bc20a627cd3c3ba8
Akademický článek
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Autor:
Ayis Pyrros, Stephen M. Borstelmann, Ramana Mantravadi, Zachary Zaiman, Kaesha Thomas, Brandon Price, Eugene Greenstein, Nasir Siddiqui, Melinda Willis, Ihar Shulhan, John Hines-Shah, Jeanne M. Horowitz, Paul Nikolaidis, Matthew P. Lungren, Jorge Mario Rodríguez-Fernández, Judy Wawira Gichoya, Sanmi Koyejo, Adam E Flanders, Nishith Khandwala, Amit Gupta, John W. Garrett, Joseph Paul Cohen, Brian T. Layden, Perry J. Pickhardt, William Galanter
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-12 (2023)
Abstract Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes
Externí odkaz:
https://doaj.org/article/7a733957020f47618b736360b6cd8f9b
Autor:
Zhu, Yingying, Tang, Youbao, Tang, Yuxing, Elton, Daniel C., Lee, Sungwon, Pickhardt, Perry J., Summers, Ronald M.
Current deep learning based segmentation models often generalize poorly between domains due to insufficient training data. In real-world clinical applications, cross-domain image analysis tools are in high demand since medical images from different d
Externí odkaz:
http://arxiv.org/abs/2007.07230
Calcified plaque in the aorta and pelvic arteries is associated with coronary artery calcification and is a strong predictor of heart attack. Current calcified plaque detection models show poor generalizability to different domains (ie. pre-contrast
Externí odkaz:
http://arxiv.org/abs/2005.11384