Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Pareek, Anuj"'
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53d169bf9bddbe8b63fe901f5b90481f
http://arxiv.org/abs/2304.00487
http://arxiv.org/abs/2304.00487
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8ff3e70c200612c80263f6a3b47702e2
http://arxiv.org/abs/2102.09475
http://arxiv.org/abs/2102.09475
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4809df4a327a9ad2393c2d377485694
http://arxiv.org/abs/2007.06199
http://arxiv.org/abs/2007.06199
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a8b44e67d2df58ce0b89abe1492bb9c
http://arxiv.org/abs/2002.11379
http://arxiv.org/abs/2002.11379
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e43c6cdca81e2a702a202bbb1f6b9f7