Zobrazeno 1 - 10
of 5 138
pro vyhledávání: '"Lo, A. Y."'
Autor:
Yang, Julia, Barnett, Alina Jade, Donnelly, Jon, Kishore, Satvik, Fang, Jerry, Schwartz, Fides Regina, Chen, Chaofan, Lo, Joseph Y., Rudin, Cynthia
Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models
Externí odkaz:
http://arxiv.org/abs/2406.06386
Autor:
Dahal, Lavsen, Ghojoghnejad, Mobina, Ghosh, Dhrubajyoti, Bhandari, Yubraj, Kim, David, Ho, Fong Chi, Tushar, Fakrul Islam, Luoa, Sheng, Lafata, Kyle J., Abadi, Ehsan, Samei, Ehsan, Lo, Joseph Y., Segars, W. Paul
Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VITs. However, the current librarie
Externí odkaz:
http://arxiv.org/abs/2405.11133
Autor:
Tushar, Fakrul Islam, Wang, Avivah, Dahal, Lavsen, Harowicz, Michael R., Lafata, Kyle J., Tailor, Tina D., Lo, Joseph Y.
Lung cancer's high mortality rate can be mitigated by early detection, increasingly reliant on AI for diagnostic imaging. However, AI model performance depends on training and validation datasets. This study develops and validates AI models for both
Externí odkaz:
http://arxiv.org/abs/2405.04605
The way in which a social network is generated, in terms of how individuals attach to each other, determines the properties of the resulting network. Here we study an intuitively appealing `friend of a friend' model, where a network is formed by each
Externí odkaz:
http://arxiv.org/abs/2404.14205
Autor:
Tushar, Fakrul Islam, Vancoillie, Liesbeth, McCabe, Cindy, Kavuri, Amareswararao, Dahal, Lavsen, Harrawood, Brian, Fryling, Milo, Zarei, Mojtaba, Sotoudeh-Paima, Saman, Ho, Fong Chi, Ghosh, Dhrubajyoti, Harowicz, Michael R., Tailor, Tina D., Luo, Sheng, Segars, W. Paul, Abadi, Ehsan, Lafata, Kyle J., Lo, Joseph Y., Samei, Ehsan
Objectives: To demonstrate that a virtual imaging trial platform can accurately emulate a major clinical trial, specifically the National Lung Screening Trial (NLST) that compared computed tomography (CT) and chest radiography (CXR) imaging for lung
Externí odkaz:
http://arxiv.org/abs/2404.11221
Weakly supervised learning with noisy data has drawn attention in the medical imaging community due to the sparsity of high-quality disease labels. However, little is known about the limitations of such weakly supervised learning and the effect of th
Externí odkaz:
http://arxiv.org/abs/2402.04419
Publikováno v:
Combinator. Probab. Comp. 34 (2025) 90-114
We use Stein's method to obtain distributional approximations of subgraph counts in the uniform attachment model or random directed acyclic graph; we provide also estimates of rates of convergence. In particular, we give uni- and multi-variate Poisso
Externí odkaz:
http://arxiv.org/abs/2311.04184
Autor:
Mouheb, Kaouther, Nejad, Mobina Ghojogh, Dahal, Lavsen, Samei, Ehsan, Lafata, Kyle J., Segars, W. Paul, Lo, Joseph Y.
Accurate 3D modeling of human organs plays a crucial role in building computational phantoms for virtual imaging trials. However, generating anatomically plausible reconstructions of organ surfaces from computed tomography scans remains challenging f
Externí odkaz:
http://arxiv.org/abs/2309.08289
Autor:
Tushar, Fakrul Islam, Dahal, Lavsen, Sotoudeh-Paima, Saman, Abadi, Ehsan, Segars, W. Paul, Samei, Ehsan, Lo, Joseph Y.
The credibility of Artificial Intelligence (AI) models in medical imaging, particularly during the COVID-19 pandemic, has been challenged by reproducibility issues and obscured clinical insights. To address these concerns, we propose a Virtual Imagin
Externí odkaz:
http://arxiv.org/abs/2308.09730
Autor:
Soublière, Jean-François1 (AUTHOR) jf.soubliere@hec.ca, Lo, Jade Y.2 (AUTHOR) yl663@drexel.edu, Rhee, Eunice Y.3 (AUTHOR) rhee@seattleu.edu
Publikováno v:
Academy of Management Journal. Feb2024, Vol. 67 Issue 1, p61-91. 31p.
