Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Pareek, Anuj"'
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:
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
Autor:
Cohen, Joseph Paul, Brooks, Rupert, En, Sovann, Zucker, Evan, Pareek, Anuj, Lungren, Matthew, Chaudhari, Akshay
This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays. We conduct a reader study with two radiologists assessing 240 chest X-ray predictions to rate their confidence that the model's prediction is correc
Externí odkaz:
http://arxiv.org/abs/2304.00487
Autor:
Wang, Chen, Johansson, Anna L.V., Nyberg, Cina, Pareek, Anuj, Almqvist, Catarina, Hernandez-Diaz, Sonia, Oberg, Anna S.
Publikováno v:
In Fertility and Sterility July 2024 122(1):95-105
Autor:
Cohen, Joseph Paul, Brooks, Rupert, En, Sovann, Zucker, Evan, Pareek, Anuj, Lungren, Matthew P., Chaudhari, Akshay
Motivation: Traditional image attribution methods struggle to satisfactorily explain predictions of neural networks. Prediction explanation is important, especially in medical imaging, for avoiding the unintended consequences of deploying AI systems
Externí odkaz:
http://arxiv.org/abs/2102.09475
Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise. However, poor generalization due to data distribution shifts in clinical setti
Externí odkaz:
http://arxiv.org/abs/2102.08660
Autor:
Rajpurkar, Pranav, Joshi, Anirudh, Pareek, Anuj, Irvin, Jeremy, Ng, Andrew Y., Lungren, Matthew
The use of smartphones to take photographs of chest x-rays represents an appealing solution for scaled deployment of deep learning models for chest x-ray interpretation. However, the performance of chest x-ray algorithms on photos of chest x-rays has
Externí odkaz:
http://arxiv.org/abs/2011.06129
Autor:
Phillips, Nick A., Rajpurkar, Pranav, Sabini, Mark, Krishnan, Rayan, Zhou, Sharon, Pareek, Anuj, Phu, Nguyet Minh, Wang, Chris, Jain, Mudit, Du, Nguyen Duong, Truong, Steven QH, Ng, Andrew Y., Lungren, Matthew P.
Clinical deployment of deep learning algorithms for chest x-ray interpretation requires a solution that can integrate into the vast spectrum of clinical workflows across the world. An appealing approach to scaled deployment is to leverage the ubiquit
Externí odkaz:
http://arxiv.org/abs/2007.06199
Autor:
Smit, Akshay, Jain, Saahil, Rajpurkar, Pranav, Pareek, Anuj, Ng, Andrew Y., Lungren, Matthew P.
The extraction of labels from radiology text reports enables large-scale training of medical imaging models. Existing approaches to report labeling typically rely either on sophisticated feature engineering based on medical domain knowledge or manual
Externí odkaz:
http://arxiv.org/abs/2004.09167
Autor:
Rajpurkar, Pranav, Joshi, Anirudh, Pareek, Anuj, Chen, Phil, Kiani, Amirhossein, Irvin, Jeremy, Ng, Andrew Y., Lungren, Matthew P.
Although there have been several recent advances in the application of deep learning algorithms to chest x-ray interpretation, we identify three major challenges for the translation of chest x-ray algorithms to the clinical setting. We examine the pe
Externí odkaz:
http://arxiv.org/abs/2002.11379