Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Yiqiu Shen"'
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:
Yiqiu Shen, Laura Heacock, Jonathan Elias, Keith D. Hentel, Beatriu Reig, George Shih, Linda Moy
Publikováno v:
Radiology. 307
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3571e7b10e4f942852f0219917518696
https://doi.org/10.1148/radiol.230163
https://doi.org/10.1148/radiol.230163
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/
Deep learning in the presence of noisy annotations has been studied extensively in classification, but much less in segmentation tasks. In this work, we study the learning dynamics of deep segmentation networks trained on inaccurately-annotated data.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fad16228fe2610fa39ff053fd09a572f
Autor:
Meng Cao, Yindalon Aphinyanaphongs, Jan Witowski, Carlos Fernandez-Granda, Yiqiu Shen, Siddhant Dogra, Duo Wang, Jungkyu Park, Narges Razavian, David Kudlowitz, Krzysztof J. Geras, Yvonne W. Lui, Farah E. Shamout, Nan Wu, Lea Azour, Aakash Kaku, Stanisław Jastrzębski, William Moore, Taro Makino, Ben Zhang
Publikováno v:
ArXiv
npj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
NPJ Digital Medicine
npj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
NPJ Digital Medicine
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 risk using a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f39f3f0742be77aea34cfa88df9262d
https://europepmc.org/articles/PMC7418753/
https://europepmc.org/articles/PMC7418753/
Autor:
Krzysztof J. Geras, Cem M. Deniz, Kyunghyun Cho, Gregory Chang, Kevin Leung, Yiqiu Shen, Bofei Zhang, James S. Babb, Jimin Tan
Publikováno v:
Radiology
BACKGROUND: The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. PURPOSE: To develop a deep learning (DL) prediction model for risk of OA progression by us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0761511c1e596267b83d95c7361e8335
https://pubmed.ncbi.nlm.nih.gov/32579090
https://pubmed.ncbi.nlm.nih.gov/32579090
Autor:
Yiqiu Shen, Jason Phang, Nan Wu, S. Gene Kim, Krzysztof J. Geras, Linda Moy, Jungkyu Park, Kyunghyun Cho
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783030326913
MLMI@MICCAI
MLMI@MICCAI
Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and sma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbeeecacdebcbfa292c571b47240b9f6
https://doi.org/10.1007/978-3-030-32692-0_3
https://doi.org/10.1007/978-3-030-32692-0_3
Autor:
Naziya Samreen, Beatriu Reig, Kara Ho, Kyunghyun Cho, Jungkyu Park, Laura Heacock, Zhe Huang, Sushma Gaddam, Eric Kim, Yiming Gao, Linda Moy, Joshua D. Weinstein, Jason Phang, Nan Wu, Jiyon Lee, Yiqiu Shen, Alana A. Lewin, Masha Zorin, Ujas Parikh, Krzysztof J. Geras, S. Gene Kim, Krystal Airola, Stacey Wolfson, Hildegard B. Toth, Stephanie H Chung, Joe Katsnelson, Thibault Févry, Eralda Mema, Leng Leng Young Lin, Kristine Pysarenko, Esther Hwang, Stanisław Jastrzębski
Publikováno v:
IEEE transactions on medical imaging
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting whether there is a cancer in the b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53329e18a25d244b7312111ada30eecd
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