Zobrazeno 1 - 10
of 413
pro vyhledávání: '"Daniel L. Rubin"'
Autor:
Khaled Saab, Siyi Tang, Mohamed Taha, Christopher Lee-Messer, Christopher Ré, Daniel L. Rubin
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-9 (2024)
Abstract A major barrier to deploying healthcare AI is trustworthiness. One form of trustworthiness is a model’s robustness across subgroups: while models may exhibit expert-level performance on aggregate metrics, they often rely on non-causal feat
Externí odkaz:
https://doaj.org/article/1fd90123b45c4a549e735c5d951c7690
Autor:
Laurel A. Habel, Stacey E. Alexeeff, Ninah Achacoso, Vignesh A. Arasu, Aimilia Gastounioti, Lawrence Gerstley, Robert J. Klein, Rhea Y. Liang, Jafi A. Lipson, Walter Mankowski, Laurie R. Margolies, Joseph H. Rothstein, Daniel L. Rubin, Li Shen, Adriana Sistig, Xiaoyu Song, Marvella A. Villaseñor, Mark Westley, Alice S. Whittemore, Martin J. Yaffe, Pei Wang, Despina Kontos, Weiva Sieh
Publikováno v:
Breast Cancer Research, Vol 25, Iss 1, Pp 1-9 (2023)
Abstract Background Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long
Externí odkaz:
https://doaj.org/article/c80b8409fd6744bfba5b869ab41e08e7
Autor:
Stephen M. Moore, James D. Quirk, Andrew W. Lassiter, Richard Laforest, Gregory D. Ayers, Cristian T. Badea, Andriy Y. Fedorov, Paul E. Kinahan, Matthew Holbrook, Peder E. Z. Larson, Renuka Sriram, Thomas L. Chenevert, Dariya Malyarenko, John Kurhanewicz, A. McGarry Houghton, Brian D. Ross, Stephen Pickup, James C. Gee, Rong Zhou, Seth T. Gammon, Henry Charles Manning, Raheleh Roudi, Heike E. Daldrup-Link, Michael T. Lewis, Daniel L. Rubin, Thomas E. Yankeelov, Kooresh I. Shoghi
Publikováno v:
Tomography, Vol 9, Iss 3, Pp 995-1009 (2023)
Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute’s (NCI) precision medicine initiative emphasizes the use
Externí odkaz:
https://doaj.org/article/c03b45b43e1546d4a22efc4f2e820217
Autor:
Emel Alkim, Heidi Dowst, Julie DiCarlo, Lacey E. Dobrolecki, Anadulce Hernández-Herrera, David A. Hormuth, Yuxing Liao, Apollo McOwiti, Robia Pautler, Mothaffar Rimawi, Ashley Roark, Ramakrishnan Rajaram Srinivasan, Jack Virostko, Bing Zhang, Fei Zheng, Daniel L. Rubin, Thomas E. Yankeelov, Michael T. Lewis
Publikováno v:
Tomography, Vol 9, Iss 2, Pp 810-828 (2023)
Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) us
Externí odkaz:
https://doaj.org/article/e0d87f8b7a8e4f92951123a867d724ef
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-14 (2022)
Randomized clinical trials are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding factors. Here, the authors develop a framework based on natural language processing to uncover interpretable pote
Externí odkaz:
https://doaj.org/article/9793d56191ac4479ae2041aa5b42617a
Autor:
Jon André Ottesen, Darvin Yi, Elizabeth Tong, Michael Iv, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel L. Rubin, Atle Bjørnerud, Greg Zaharchuk, Endre Grøvik
Publikováno v:
Frontiers in Neuroinformatics, Vol 16 (2023)
IntroductionManagement of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segm
Externí odkaz:
https://doaj.org/article/bec8b73eb0fa4d7f918f7a95d9a3719f
Autor:
Aalok Patwa, Rikiya Yamashita, Jin Long, Tyler Risom, Michael Angelo, Leeat Keren, Daniel L. Rubin
Publikováno v:
Communications Biology, Vol 4, Iss 1, Pp 1-14 (2021)
Patwa, Yamashita et al. utilize multiplexed imaging to demonstrate that profiling cell-to-cell interactions in the microenvironment can reveal predictors of recurrence and overall survival in triple-negative breast cancer, especially highlighting the
Externí odkaz:
https://doaj.org/article/accef0e4e5044d8990a14811008e28a2
Autor:
Siyi Tang, Amirata Ghorbani, Rikiya Yamashita, Sameer Rehman, Jared A. Dunnmon, James Zou, Daniel L. Rubin
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Abstract The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a da
Externí odkaz:
https://doaj.org/article/115fcccb74764f0687b45ec863822f0b
Autor:
Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Line Brennhaug Nilsen, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel L. Rubin, Greg Zaharchuk
Publikováno v:
npj Digital Medicine, Vol 4, Iss 1, Pp 1-7 (2021)
Abstract The purpose of this study was to assess the clinical value of a deep learning (DL) model for automatic detection and segmentation of brain metastases, in which a neural network is trained on four distinct MRI sequences using an input-level d
Externí odkaz:
https://doaj.org/article/6170801e8ab643338693ba66c6b19279
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
Abstract Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However,
Externí odkaz:
https://doaj.org/article/28c1b7e83175401e8131ebcd53e2459a