Zobrazeno 1 - 10
of 857
pro vyhledávání: '"David Gruen"'
Autor:
Romano, Giorgio
Publikováno v:
La Rassegna Mensile di Israel, 1968 Jan 01. 34(1), 32-36.
Externí odkaz:
https://www.jstor.org/stable/41282652
Autor:
Chapman, Bruce J.
Publikováno v:
Economic Analysis and Policy; September 1988, Vol. 18 Issue: 2 p171-188, 18p
Autor:
Rochelle F. Andreotti, Elizabeth K. Arleo, Sandeep S. Arora, Jennifer C. Broder, Olga Brook, Erin A. Cooke, Melissa A. Davis, Katia Dodelzon, Meridith J. Englander, Nancy J. Fischbein, Arthur Fleischer, Katherine Frederick-Dyer, Rachel F. Gerson, David Gruen, Elizabeth M. Hecht, Janine T. Katzen, Jennifer Kemp, Amy L. Kotsenas, Lauren M. Ladd, Anjali Malik, Geraldine McGinty, Carolyn C. Meltzer, Amy Oliveira, Catherine Phillips, Kristin K. Porter, Patricia Rhyner, Caroline Robson, Deborah Shatzkes, Lucy B. Spalluto, Maryellen Sun, Courtney Tomblinson, Nina S. Vincoff, Monica J. Wood, Beth Zigmund, Christine Glastonbury, Jana Ivanidze, Erin Simon Schwartz, Pamela K. Woodard
Publikováno v:
Clinical Imaging. 89:95-96
Autor:
Vesna Barros, Tal Tlusty, Ella Barkan, Efrat Hexter, David Gruen, Michal Guindy, Michal Rosen-Zvi
Publikováno v:
Radiology
Background: Computational models based on artificial intelligence (AI) are increasingly used to diagnose malignant breast le-sions. However, assessment from radiologic images of the specific pathologic lesion subtypes, as detailed in the results of b
Publikováno v:
Chest. 162:A952
Publikováno v:
ISBI
Pneumoperitoneum (free air in the peritoneal cavity) is a rare condition that can be life threatening and require emergency surgery. It can be detected in chest X-ray but there are some challenges associated to this detection, such as small amounts o
Autor:
Tal Tlusty, Michal Rosen-Zvi, Mika Amit, Michal Guindy, Ella Barkan, Vesna Resende Barros, David Gruen, Michal Ozery-Flato, Efrat Hexter, Mona Rozin, Tal Arazi
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783030875886
MLMI@MICCAI
MLMI@MICCAI
Characterization of lesions by artificial intelligence (AI) has been the subject of extensive research. In recent years, many studies demonstrated the ability of convolution neural networks (CNNs) to successfully distinguish between malignant and ben
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23065141284f2515aedab0f9b4112567
https://doi.org/10.1007/978-3-030-87589-3_29
https://doi.org/10.1007/978-3-030-87589-3_29
Autor:
Marvyn Allen Chan, David Gruen, Patrick Benjamin, Olamide Oyenubi, Bisrat Teweldemedhin, Gerald Kristoffer E. Boy, Thomas J. Pettei
Publikováno v:
Journal of the American College of Cardiology. 79:3221
Publikováno v:
Journal of the American Medical Informatics Association : JAMIA
Objective To conduct a systematic review identifying workplace interventions that mitigate physician burnout related to the digital environment including health information technologies (eg, electronic health records) and decision support systems) wi
Autor:
Lance Miller, J. Patrick Henry, C. C. Kirkpatrick, N Cibirka, David Gruen, Eduardo S. Cypriano, Huanyuan Shan, F. Brimioulle, Jean-Paul Kneib, L. van Waerbeke, Thomas Erben, Eli S. Rykoff, Alexis Finoguenov, P. Spinelli, Eduardo Rozo, R. A. Dupke
Publikováno v:
Mon.Not.Roy.Astron.Soc.
Mon.Not.Roy.Astron.Soc., 2017, 468 (1), pp.1092-1116. 〈10.1093/mnras/stx484〉
Mon.Not.Roy.Astron.Soc., 2017, 468 (1), pp.1092-1116. ⟨10.1093/mnras/stx484⟩
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Monthly Notices of the Royal Astronomical Society
Monthly Notices of the Royal Astronomical Society, 2017, 468 (1), pp.1092-1116. ⟨10.1093/mnras/stx484⟩
Mon.Not.Roy.Astron.Soc., 2017, 468 (1), pp.1092-1116. 〈10.1093/mnras/stx484〉
Mon.Not.Roy.Astron.Soc., 2017, 468 (1), pp.1092-1116. ⟨10.1093/mnras/stx484⟩
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Monthly Notices of the Royal Astronomical Society
Monthly Notices of the Royal Astronomical Society, 2017, 468 (1), pp.1092-1116. ⟨10.1093/mnras/stx484⟩
We present a stacked weak lensing analysis of 27 richness selected galaxy clusters at $0.40 \leqslant z \leqslant 0.62$ in the CODEX survey. The fields were observed in 5 bands with the CFHT. We measure the stacked surface mass density profile with a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c442d2f459845a1ac589fdf38dd5ef0
https://hal.archives-ouvertes.fr/hal-01582739
https://hal.archives-ouvertes.fr/hal-01582739