Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Krzysztof J, Geras"'
Autor:
Taro Makino, Stanisław Jastrzębski, Witold Oleszkiewicz, Celin Chacko, Robin Ehrenpreis, Naziya Samreen, Chloe Chhor, Eric Kim, Jiyon Lee, Kristine Pysarenko, Beatriu Reig, Hildegard Toth, Divya Awal, Linda Du, Alice Kim, James Park, Daniel K. Sodickson, Laura Heacock, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since they can fail for reasons unrelated to underlying pathology. Humans are less likely to make such superficial mistakes, since they us
Externí odkaz:
https://doaj.org/article/52ecbc1cae5f4d78aecf462062a6035e
Autor:
Yiqiu Shen, Farah E. Shamout, Jamie R. Oliver, Jan Witowski, Kawshik Kannan, Jungkyu Park, Nan Wu, Connor Huddleston, Stacey Wolfson, Alexandra Millet, Robin Ehrenpreis, Divya Awal, Cathy Tyma, Naziya Samreen, Yiming Gao, Chloe Chhor, Stacey Gandhi, Cindy Lee, Sheila Kumari-Subaiya, Cindy Leonard, Reyhan Mohammed, Christopher Moczulski, Jaime Altabet, James Babb, Alana Lewin, Beatriu Reig, Linda Moy, Laura Heacock, Krzysztof J. Geras
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
Ultrasound is an important imaging modality for the detection and characterization of breast cancer, but it has been noted to have high false-positive rates. Here, the authors present an artificial intelligence system that achieves radiologist-level
Externí odkaz:
https://doaj.org/article/c9426dec81ff479488e813049d5bf81a
Autor:
Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanisław Jastrzębski, Jan Witowski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, Krzysztof J. Geras
Publikováno v:
npj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
Abstract During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration ris
Externí odkaz:
https://doaj.org/article/39490bddaa344a02b06a3d77ab46dd52
Autor:
Jan Witowski, Laura Heacock, Beatriu Reig, Stella K. Kang, Alana Lewin, Kristine Pysarenko, Shalin Patel, Naziya Samreen, Wojciech Rudnicki, Elżbieta Łuczyńska, Tadeusz Popiela, Linda Moy, Krzysztof J. Geras
Publikováno v:
Science translational medicine. 14(664)
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We used a deep learning (DL) system to improve the overall accuracy of breast
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fa8ccc0167b8d27b9df04d7a1397f3a
https://pubmed.ncbi.nlm.nih.gov/36170446
https://pubmed.ncbi.nlm.nih.gov/36170446
Publikováno v:
Bioinformatics. 37:4216-4226
Motivation Registration of histology images from multiple sources is a pressing problem in large-scale studies of spatial -omics data. Researchers often perform ‘common coordinate registration’, akin to segmentation, in which samples are partitio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b980a702b6e59aec750ff739046c5e1
https://doi.org/10.1093/bioinformatics/btab447
https://doi.org/10.1093/bioinformatics/btab447
Autor:
Jan Witowski, Laura Heacock, Beatriu Reig, Stella K. Kang, Alana Lewin, Kristine Pyrasenko, Shalin Patel, Naziya Samreen, Wojciech Rudnicki, Elżbieta Łuczyńska, Tadeusz Popiela, Linda Moy, Krzysztof J. Geras
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a very high sensitivity in detecting breast cancer, but it often leads to unnecessary biopsies and patient workup. In this paper, we used an artificial intelligence (AI) system to imp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74281da772882fb23b91af06715933b3
https://doi.org/10.1101/2022.02.07.22270518
https://doi.org/10.1101/2022.02.07.22270518
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:
Linda Moy, Taro Makino, Kyunghyun Cho, Yiqiu Shen, Laura Heacock, Zhe Huang, Nan Wu, Jason Phang, Jungkyu Park, S. Gene Kim, Krzysztof J. Geras
Publikováno v:
J Digit Imaging
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we buil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6cdce670a993d586ee25810b45504d0
https://europepmc.org/articles/PMC8669066/
https://europepmc.org/articles/PMC8669066/
Autor:
Anuroop, Sriram, Matthew, Muckley, Koustuv, Sinha, Farah, Shamout, Joelle, Pineau, Krzysztof J, Geras, Lea, Azour, Yindalon, Aphinyanaphongs, Nafissa, Yakubova, William, Moore
Publikováno v:
ArXiv
The rapid spread of COVID-19 cases in recent months has strained hospital resources, making rapid and accurate triage of patients presenting to emergency departments a necessity. Machine learning techniques using clinical data such as chest X-rays ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::e2d2fdd75121b0ed61ede3743169f853
https://pubmed.ncbi.nlm.nih.gov/33469559
https://pubmed.ncbi.nlm.nih.gov/33469559
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