Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Yung, Joshua"'
Autor:
Woodland, McKell, Patel, Nihil, Castelo, Austin, Taie, Mais Al, Eltaher, Mohamed, Yung, Joshua P., Netherton, Tucker J., Calderone, Tiffany L., Sanchez, Jessica I., Cleere, Darrel W., Elsaiey, Ahmed, Gupta, Nakul, Victor, David, Beretta, Laura, Patel, Ankit B., Brock, Kristy K.
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024) 2006
Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these models tend to perform well in most instances, which could exacerbate auto
Externí odkaz:
http://arxiv.org/abs/2408.02761
Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend
Autor:
Woodland, McKell, Castelo, Austin, Taie, Mais Al, Silva, Jessica Albuquerque Marques, Eltaher, Mohamed, Mohn, Frank, Shieh, Alexander, Kundu, Suprateek, Yung, Joshua P., Patel, Ankit B., Brock, Kristy K.
Fr\'echet Inception Distance (FID) is a widely used metric for assessing synthetic image quality. It relies on an ImageNet-based feature extractor, making its applicability to medical imaging unclear. A recent trend is to adapt FID to medical imaging
Externí odkaz:
http://arxiv.org/abs/2311.13717
Autor:
McCullum, Lucas, Wood, John, Gule-Monroe, Maria, Liu, Ho-Ling Anthony, Chen, Melissa, Shah, Komal, Chasen, Noah Nathan, Kumar, Vinodh, Hou, Ping, Stafford, Jason, Chung, Caroline, Ahmad, Moiz, Walker, Christopher, Yung, Joshua
A dataset of 3D-GRE and 3D-TSE brain 3T post contrast T1-weighted images as part of a quality improvement project were collected and shown to five neuro-radiologists who evaluated each sequence for both image quality and imaging artifacts. The same s
Externí odkaz:
http://arxiv.org/abs/2311.05412
Autor:
Woodland, McKell, Patel, Nihil, Taie, Mais Al, Yung, Joshua P., Netherton, Tucker J., Patel, Ankit B., Brock, Kristy K.
Publikováno v:
In: UNSURE 2023. LNCS, vol 14291. Springer, Cham (2023)
Clinically deployed segmentation models are known to fail on data outside of their training distribution. As these models perform well on most cases, it is imperative to detect out-of-distribution (OOD) images at inference to protect against automati
Externí odkaz:
http://arxiv.org/abs/2308.03723
Autor:
Muthusivarajan, Rajarajeswari, Celaya, Adrian, Yung, Joshua P., Viswanath, Satish, Marcus, Daniel S., Chung, Caroline, Fuentes, David
Deep neural networks with multilevel connections process input data in complex ways to learn the information.A networks learning efficiency depends not only on the complex neural network architecture but also on the input training images.Medical imag
Externí odkaz:
http://arxiv.org/abs/2111.01093
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Fuentes, David, Cardan, Rex, Stafford, R. Jason, Yung, Joshua, Dodd, Gerald D., III, Feng, Yusheng
Publikováno v:
In Journal of Vascular and Interventional Radiology 2010 21(11):1725-1732
Autor:
Fahrenholtz, Samuel John, Chunxiao Guo, MacLellan, Christopher J., Yung, Joshua P., Ken-Pin Hwang, Layman, Rick R., R. Jason Stafford, Cressman, Erik
Purpose: MR temperature imaging (MRTI) was employed for visualizing the spatiotemporal evolution of the exotherm of thermoembolization, an investigative transarterial treatment for solid tumors. Materials and methods: Five explanted kidneys were inje
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bd4f0161429d7f3947a3d77e7f73cec
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.