Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Sandino, Christopher"'
Autor:
Fang, Ching, Sandino, Christopher, Mahasseni, Behrooz, Minxha, Juri, Pouransari, Hadi, Azemi, Erdrin, Moin, Ali, Zippi, Ellen
Many healthcare applications are inherently multimodal, involving several physiological signals. As sensors for these signals become more common, improving machine learning methods for multimodal healthcare data is crucial. Pretraining foundation mod
Externí odkaz:
http://arxiv.org/abs/2410.16424
Autor:
Patel, Gaurav, Sandino, Christopher, Mahasseni, Behrooz, Zippi, Ellen L, Azemi, Erdrin, Moin, Ali, Minxha, Juri
In this paper, we propose a framework for efficient Source-Free Domain Adaptation (SFDA) in the context of time-series, focusing on enhancing both parameter efficiency and data-sample utilization. Our approach introduces an improved paradigm for sour
Externí odkaz:
http://arxiv.org/abs/2410.02147
Sleep staging is a clinically important task for diagnosing various sleep disorders, but remains challenging to deploy at scale because it because it is both labor-intensive and time-consuming. Supervised deep learning-based approaches can automate s
Externí odkaz:
http://arxiv.org/abs/2404.15308
Autor:
Oscanoa, Julio A., Ong, Frank, Iyer, Siddharth S., Li, Zhitao, Sandino, Christopher M., Ozturkler, Batu, Ennis, Daniel B., Pilanci, Mert, Vasanawala, Shreyas S.
Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for 3D non-Cartesian acquisitions. One common approach is to reduce th
Externí odkaz:
http://arxiv.org/abs/2305.06482
Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for E
Externí odkaz:
http://arxiv.org/abs/2211.02625
Autor:
Ozturkler, Batu, Sahiner, Arda, Ergen, Tolga, Desai, Arjun D, Sandino, Christopher M, Vasanawala, Shreyas, Pauly, John M, Mardani, Morteza, Pilanci, Mert
Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization. However
Externí odkaz:
http://arxiv.org/abs/2207.08393
Autor:
Desai, Arjun D, Schmidt, Andrew M, Rubin, Elka B, Sandino, Christopher M, Black, Marianne S, Mazzoli, Valentina, Stevens, Kathryn J, Boutin, Robert, Ré, Christopher, Gold, Garry E, Hargreaves, Brian A, Chaudhari, Akshay S
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and difficulty extracting) quantitative indicators that are sensitive to ti
Externí odkaz:
http://arxiv.org/abs/2203.06823
Autor:
Desai, Arjun D, Ozturkler, Batu M, Sandino, Christopher M, Boutin, Robert, Willis, Marc, Vasanawala, Shreyas, Hargreaves, Brian A, Ré, Christopher M, Pauly, John M, Chaudhari, Akshay S
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particular
Externí odkaz:
http://arxiv.org/abs/2110.00075
Autor:
Wang, Ke, Kellman, Michael, Sandino, Christopher M., Zhang, Kevin, Vasanawala, Shreyas S., Tamir, Jonathan I., Yu, Stella X., Lustig, Michael
Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI). Similar to compressed sensing, DL can leverage high-dimensional data (e.g. 3D, 2D+time, 3D+time) to further
Externí odkaz:
http://arxiv.org/abs/2103.04003
Autor:
Ma, Jeffrey, Nakarmi, Ukash, Kin, Cedric Yue Sik, Sandino, Christopher, Cheng, Joseph Y., Syed, Ali B., Wei, Peter, Pauly, John M., Vasanawala, Shreyas
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by exp
Externí odkaz:
http://arxiv.org/abs/1912.02907