Zobrazeno 1 - 10
of 1 068
pro vyhledávání: '"PAULY, JOHN"'
Autor:
Van Veen, Dave, Van Uden, Cara, Blankemeier, Louis, Delbrouck, Jean-Benoit, Aali, Asad, Bluethgen, Christian, Pareek, Anuj, Polacin, Malgorzata, Reis, Eduardo Pontes, Seehofnerova, Anna, Rohatgi, Nidhi, Hosamani, Poonam, Collins, William, Ahuja, Neera, Langlotz, Curtis P., Hom, Jason, Gatidis, Sergios, Pauly, John, Chaudhari, Akshay S.
Publikováno v:
Nature Medicine, 2024
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NL
Externí odkaz:
http://arxiv.org/abs/2309.07430
Autor:
Alkan, Cagan, Mardani, Morteza, Liao, Congyu, Li, Zhitao, Vasanawala, Shreyas S., Pauly, John M.
Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we
Externí odkaz:
http://arxiv.org/abs/2306.02888
Autor:
Van Veen, Dave, Van Uden, Cara, Attias, Maayane, Pareek, Anuj, Bluethgen, Christian, Polacin, Malgorzata, Chiu, Wah, Delbrouck, Jean-Benoit, Chaves, Juan Manuel Zambrano, Langlotz, Curtis P., Chaudhari, Akshay S., Pauly, John
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, or cli
Externí odkaz:
http://arxiv.org/abs/2305.01146
Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep-learning framework, trained by radiologists' supervision, for
Externí odkaz:
http://arxiv.org/abs/2211.04703
Autor:
Van Veen, Dave, van der Sluijs, Rogier, Ozturkler, Batu, Desai, Arjun, Bluethgen, Christian, Boutin, Robert D., Willis, Marc H., Wetzstein, Gordon, Lindell, David, Vasanawala, Shreyas, Pauly, John, Chaudhari, Akshay S.
Publikováno v:
Medical Imaging with Deep Learning. 2022
We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale-agnostic, i.e. one can train over a continuous range of scales and su
Externí odkaz:
http://arxiv.org/abs/2210.08676
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
Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks. However, the underpinning inductive bias of attention is not well understood. To address this issue, this paper ana
Externí odkaz:
http://arxiv.org/abs/2205.08078
Autor:
Gunel, Beliz, Sahiner, Arda, Desai, Arjun D., Chaudhari, Akshay S., Vasanawala, Shreyas, Pilanci, Mert, Pauly, John
Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task. However, these approaches depend on fully-sampled scans as ground tru
Externí odkaz:
http://arxiv.org/abs/2204.10436
In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate either patients have to return for an additional scan, or a suboptimal interpretation is rendered. An automatic image quality asse
Externí odkaz:
http://arxiv.org/abs/2111.03780
Autor:
Desai, Arjun D, Gunel, Beliz, Ozturkler, Batu M, Beg, Harris, Vasanawala, Shreyas, Hargreaves, Brian A, Ré, Christopher, Pauly, John M, Chaudhari, Akshay S
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction. However, such models require a large number of fully-sampled ground
Externí odkaz:
http://arxiv.org/abs/2111.02549