Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Shah, Meet P."'
Autor:
Pedemonte, Stefano, Tsue, Trevor, Mombourquette, Brent, Vu, Yen Nhi Truong, Matthews, Thomas, Hoil, Rodrigo Morales, Shah, Meet, Ghare, Nikita, Zingman-Daniels, Naomi, Holley, Susan, Appleton, Catherine M., Su, Jason, Wahl, Richard L.
Screening mammography improves breast cancer outcomes by enabling early detection and treatment. However, false positive callbacks for additional imaging from screening exams cause unnecessary procedures, patient anxiety, and financial burden. This w
Externí odkaz:
http://arxiv.org/abs/2204.06671
Autor:
Shah, Meet, Huang, Zhiling, Laddha, Ankit, Langford, Matthew, Barber, Blake, Zhang, Sidney, Vallespi-Gonzalez, Carlos, Urtasun, Raquel
In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps. Automotive radar provides rich, complementary information, allowing
Externí odkaz:
http://arxiv.org/abs/2010.00731
Autor:
Liu, Jerry, Wang, Shenlong, Ma, Wei-Chiu, Shah, Meet, Hu, Rui, Dhawan, Pranaab, Urtasun, Raquel
We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. Unlike prior learning-based approaches, we reduce complexity by not performing any form of explicit transformatio
Externí odkaz:
http://arxiv.org/abs/2008.09180
Autor:
Singh, Sadanand, Matthews, Thomas Paul, Shah, Meet, Mombourquette, Brent, Tsue, Trevor, Long, Aaron, Almohsen, Ranya, Pedemonte, Stefano, Su, Jason
Mammography-based screening has helped reduce the breast cancer mortality rate, but has also been associated with potential harms due to low specificity, leading to unnecessary exams or procedures, and low sensitivity. Digital breast tomosynthesis (D
Externí odkaz:
http://arxiv.org/abs/2001.08381
Autor:
Pedemonte, Stefano, Mombourquette, Brent, Goh, Alexis, Tsue, Trevor, Long, Aaron, Singh, Sadanand, Matthews, Thomas Paul, Shah, Meet, Su, Jason
Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate; however, there are not enough radiologists to serve the growing population of women seeking screening mammography. Although commercial computer
Externí odkaz:
http://arxiv.org/abs/2001.08382
Autor:
Matthews, Thomas P., Singh, Sadanand, Mombourquette, Brent, Su, Jason, Shah, Meet P., Pedemonte, Stefano, Long, Aaron, Maffit, David, Gurney, Jenny, Hoil, Rodrigo Morales, Ghare, Nikita, Smith, Douglas, Moore, Stephen M., Marks, Susan C., Wahl, Richard L.
Purpose: To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density deep learning (DL) model in a multi-site setting for synthetic two-dimensional mammography (SM) images derived from digital breast tomosynthesis exams using full-
Externí odkaz:
http://arxiv.org/abs/2001.08383
Autor:
Singh, Amanpreet, Natarajan, Vivek, Shah, Meet, Jiang, Yu, Chen, Xinlei, Batra, Dhruv, Parikh, Devi, Rohrbach, Marcus
Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today's VQA models can not read! Our paper takes a first step towards addressing this probl
Externí odkaz:
http://arxiv.org/abs/1904.08920
Despite significant progress in Visual Question Answering over the years, robustness of today's VQA models leave much to be desired. We introduce a new evaluation protocol and associated dataset (VQA-Rephrasings) and show that state-of-the-art VQA mo
Externí odkaz:
http://arxiv.org/abs/1902.05660
For medical image segmentation, most fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, which is taxing in terms of costs, time and logistics involved. This burden of annotation can
Externí odkaz:
http://arxiv.org/abs/1812.11302
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