Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Ella Barkan"'
Autor:
Itske Fraterman, Barbara M Wollersheim, Valentina Tibollo, Savannah Lucia Catherina Glaser, Stephanie Medlock, Ronald Cornet, Matteo Gabetta, Vitali Gisko, Ella Barkan, Nicola di Flora, David Glasspool, Alexandra Kogan, Giordano Lanzola, Roy Leizer, Henk Mallo, Manuel Ottaviano, Mor Peleg, Lonneke V van de Poll-Franse, Nicole Veggiotti, Konrad Śniatała, Szymon Wilk, Enea Parimbelli, Silvana Quaglini, Mimma Rizzo, Laura Deborah Locati, Annelies Boekhout, Lucia Sacchi, Sofie Wilgenhof
Publikováno v:
JMIR Research Protocols, Vol 12, p e49252 (2023)
BackgroundSince treatment with immune checkpoint inhibitors (ICIs) is becoming standard therapy for patients with high-risk and advanced melanoma, an increasing number of patients experience treatment-related adverse events such as fatigue. Until now
Externí odkaz:
https://doaj.org/article/b2f5a42380df471ca9efe62cef43cf91
Autor:
Ella Barkan, Camillo Porta, Simona Rabinovici-Cohen, Valentina Tibollo, Silvana Quaglini, Mimma Rizzo
Publikováno v:
Frontiers in Oncology, Vol 13 (2023)
Background and objectivesInvestigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate the capacity of emerging A
Externí odkaz:
https://doaj.org/article/881cd6426bc34d7abb6cea60219d527d
Autor:
Yoel Shoshan, Ran Bakalo, Flora Gilboa-Solomon, Vadim Ratner, Ella Barkan, Michal Ozery-Flato, Mika Amit, Daniel Khapun, Emily B. Ambinder, Eniola T. Oluyemi, Babita Panigrahi, Philip A. DiCarlo, Michal Rosen-Zvi, Lisa A. Mullen
Publikováno v:
Radiology. 303:69-77
Background Digital breast tomosynthesis (DBT) has higher diagnostic accuracy than digital mammography, but interpretation time is substantially longer. Artificial intelligence (AI) could improve reading efficiency. Purpose To evaluate the use of AI t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f78280815d2cb0a42886763e65b37eaf
https://doi.org/10.1148/radiol.211105
https://doi.org/10.1148/radiol.211105
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0e8f652779a0605a13a8d1cb20bc920
Autor:
Nicholas Konz, Mateusz Buda, Hanxue Gu, Ashirbani Saha, Jichen Yang, Jakub Chłędowski, Jungkyu Park, Jan Witowski, Krzysztof J. Geras, Yoel Shoshan, Flora Gilboa-Solomon, Daniel Khapun, Vadim Ratner, Ella Barkan, Michal Ozery-Flato, Robert Martí, Akinyinka Omigbodun, Chrysostomos Marasinou, Noor Nakhaei, William Hsu, Pranjal Sahu, Md Belayat Hossain, Juhun Lee, Carlos Santos, Artur Przelaskowski, Jayashree Kalpathy-Cramer, Benjamin Bearce, Kenny Cha, Keyvan Farahani, Nicholas Petrick, Lubomir Hadjiiski, Karen Drukker, Samuel G. Armato, Maciej A. Mazurowski
Publikováno v:
JAMA Network Open. 6:e230524
ImportanceAn accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide.ObjectivesTo make training and ev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a8bb3df9af63065ca22b80acdc02e3c
https://doi.org/10.1001/jamanetworkopen.2023.0524
https://doi.org/10.1001/jamanetworkopen.2023.0524
Autor:
Roie Melamed, Alon Hazan, Esma Herzel, Shaked Naor, Gideon Koren, Ayelet Akselrod-Ballin, Yaara Goldschmidt, Michal Rosen-Zvi, Michal Chorev, Adam Spiro, Ehud Karavani, Yoel Shoshan, Michal Guindy, Varda Shalev, Ella Barkan
Publikováno v:
Radiology. 292:331-342
Background Computational models on the basis of deep neural networks are increasingly used to analyze health care data. However, the efficacy of traditional computational models in radiology is a matter of debate. Purpose To evaluate the accuracy and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d00f4b30af27c0b93a8db960452deb4f
https://doi.org/10.1148/radiol.2019182622
https://doi.org/10.1148/radiol.2019182622
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
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872397
MICCAI (5)
MICCAI (5)
Detecting the specific locations of malignancy signs in a medical image is a non-trivial and time-consuming task for radiologists. A complex, 3D version of this task, was presented in the DBTex 2021 Grand Challenge on Digital Breast Tomosynthesis Les
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fa6866820441369f3fe60ddf1a78f3ac
https://doi.org/10.1007/978-3-030-87240-3_74
https://doi.org/10.1007/978-3-030-87240-3_74
Autor:
Kristine Pysarenko, Pablo Gómez del Campo, Daniel Khapun, Alana A. Lewin, Linda Moy, Jungkyu Park, Yoel Shoshan, Sindhoora Murthy, Julia E. Goldberg, Robert Martí, Ella Barkan, Linda Du, Jakub Chłędowski, Ujas Parikh, Anastasia Plaunova, Krzysztof J. Geras, Sardius Chen, Alexandra Millet, Laura Heacock, Sushma Gaddam, Melanie Wegener, Eric H. Kim, Vadim Ratner, Beatriu Reig, Shalin Patel, Sana Hava, Jan Witowski, Stacey Wolfson, Michal Rosen-Zvi, Aviad Zlotnick, Jiyon Lee, Flora Gilboa-Solomon
Publikováno v:
Nature Machine Intelligence. 3:735-736
A new international competition aims to speed up the development of AI models that can assist radiologists in detecting suspicious lesions from hundreds of millions of pixels in 3D mammograms. The top three winning teams compare notes.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3db01a6dfb3f257c150bf6969fb0e791
https://doi.org/10.1038/s42256-021-00378-z
https://doi.org/10.1038/s42256-021-00378-z
Publikováno v:
Interpretable and Annotation-Efficient Learning for Medical Image Computing ISBN: 9783030611651
iMIMIC/MIL3iD/LABELS@MICCAI
iMIMIC/MIL3iD/LABELS@MICCAI
Cancer prediction models, which deeply impact human lives, must provide explanations for their predictions. We study a simple extension of a cancer mammogram classifier, trained with image-level annotations, to facilitate the built-in generation of p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b87d631b55ae4b000dc69c00de0a7ab2
https://doi.org/10.1007/978-3-030-61166-8_4
https://doi.org/10.1007/978-3-030-61166-8_4