Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Joyce Cahoon"'
Autor:
Sunny Gakhar, Joyce Cahoon, Wangchao Le, Xiangnan Li, Kaushik Ravichandran, Hiren Patel, Marc Friedman, Brandon Haynes, Shi Qiao, Alekh Jindal, Jyoti Leeka
Publikováno v:
Proceedings of the VLDB Endowment. 15:3710-3713
We demonstrate Pipemizer , an optimizer and recommender aimed at improving the performance of queries or jobs in pipelines. These job pipelines are ubiquitous in modern data analytics due to jobs reading output files written by other jobs. Given that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c381f0b58d8d21e039142594d7799fe
https://doi.org/10.14778/3554821.3554881
https://doi.org/10.14778/3554821.3554881
Publikováno v:
Veterinary and Comparative Oncology. 17:562-569
The "gold standard" for verification of patient positioning before linear accelerator-based stereotactic radiation therapy is kilovoltage cone-beam computed tomography (kV-CBCT), which is not uniformly available or utilized; planar imaging is sometim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2204a8bdc43de995ab2b81fae2d5007a
https://doi.org/10.1111/vco.12522
https://doi.org/10.1111/vco.12522
Autor:
Ryan Martin, Joyce Cahoon
Inferential challenges that arise when data are censored have been extensively studied under the classical frameworks. In this paper, we provide an alternative generalized inferential model approach whose output is a data-dependent plausibility funct
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::87d9685ce4c26544018e44b99a2ba701
Autor:
Hyo-Eun Kim, Jiashi Feng, Stephen H. Friend, Ljubomir Buturovic, Dezső Ribli, Luis Caballero, Li Shen, Fredrik Strand, Yaroslav Nikulin, Krzysztof J. Geras, Kyunghyun Cho, Elias Chaibub Neto, Rami Ben-Ari, Christoph I. Lee, Zequn Jie, Imane Nedjar, Felix Nensa, Darvin Yi, Shivanthan A.C. Yohanandan, Bruce Hoff, Justin Guinney, Jaime S. Cardoso, Russell B. McBride, Mengling Feng, Yiqiu Shen, Simona Rabinovici-Cohen, Ethan Goan, Stefan Harrer, Sven Koitka, Michael Kawczynski, Hari Trivedi, Karl Trygve Kalleberg, Christoph M. Friedrich, F. Albiol, Dimitri Perrin, Jose Costa Pereira, Umar Asif, Bibo Shi, Zbigniew Wojna, Antonio Jimeno Yepes, Peter Lindholm, Berkman Sahiner, Sijia Wang, Thea Norman, Weiva Sieh, Joyce Cahoon, Gerard Cardoso Negrie, Pavitra Krishnaswamy, Diana S. M. Buist, Alberto Albiol, Lester Mackey, Hwejin Jung, Laurie R. Margolies, Gaurav Pandey, Can Son Khoo, William Lotter, Yuanfang Guan, Thomas Yu, Andrew D. Trister, Stephen Morrell, Gustavo Stolovitzky, A. Gregory Sorensen, Clinton Fookes, Mehmet Eren Ahsen, David D. Cox, Jae Ho Sohn, Hao Du, Thomas Schaffter, Joseph H. Rothstein, Eduardo Castro, Joseph Y. Lo, Daniel L. Rubin, Obioma Pelka
Publikováno v:
JAMA Network Open
Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evalua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c12077daf27f336825279d2831286b1
https://doi.org/10.1001/jamanetworkopen.2020.0265
https://doi.org/10.1001/jamanetworkopen.2020.0265