Zobrazeno 1 - 10
of 148
pro vyhledávání: '"Cohen, Joseph Paul"'
Autor:
Blankemeier, Louis, Cohen, Joseph Paul, Kumar, Ashwin, Van Veen, Dave, Gardezi, Syed Jamal Safdar, Paschali, Magdalini, Chen, Zhihong, Delbrouck, Jean-Benoit, Reis, Eduardo, Truyts, Cesar, Bluethgen, Christian, Jensen, Malte Engmann Kjeldskov, Ostmeier, Sophie, Varma, Maya, Valanarasu, Jeya Maria Jose, Fang, Zhongnan, Huo, Zepeng, Nabulsi, Zaid, Ardila, Diego, Weng, Wei-Hung, Junior, Edson Amaro, Ahuja, Neera, Fries, Jason, Shah, Nigam H., Johnston, Andrew, Boutin, Robert D., Wentland, Andrew, Langlotz, Curtis P., Hom, Jason, Gatidis, Sergios, Chaudhari, Akshay S.
Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current radiologist shortage, there is a large impetus to use artificial intelligence to alleviate the
Externí odkaz:
http://arxiv.org/abs/2406.06512
Autor:
Chen, Zhihong, Varma, Maya, Delbrouck, Jean-Benoit, Paschali, Magdalini, Blankemeier, Louis, Van Veen, Dave, Valanarasu, Jeya Maria Jose, Youssef, Alaa, Cohen, Joseph Paul, Reis, Eduardo Pontes, Tsai, Emily B., Johnston, Andrew, Olsen, Cameron, Abraham, Tanishq Mathew, Gatidis, Sergios, Chaudhari, Akshay S., Langlotz, Curtis
Chest X-rays (CXRs) are the most frequently performed imaging test in clinical practice. Recent advances in the development of vision-language foundation models (FMs) give rise to the possibility of performing automated CXR interpretation, which can
Externí odkaz:
http://arxiv.org/abs/2401.12208
Models driven by spurious correlations often yield poor generalization performance. We propose the counterfactual (CF) alignment method to detect and quantify spurious correlations of black box classifiers. Our methodology is based on counterfactual
Externí odkaz:
http://arxiv.org/abs/2312.02186
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:
Azad, Reza, Aghdam, Ehsan Khodapanah, Rauland, Amelie, Jia, Yiwei, Avval, Atlas Haddadi, Bozorgpour, Afshin, Karimijafarbigloo, Sanaz, Cohen, Joseph Paul, Adeli, Ehsan, Merhof, Dorit
Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optim
Externí odkaz:
http://arxiv.org/abs/2211.14830
Autor:
Soin, Arjun, Merkow, Jameson, Long, Jin, Cohen, Joseph Paul, Saligrama, Smitha, Kaiser, Stephen, Borg, Steven, Tarapov, Ivan, Lungren, Matthew P
Clinical Artificial lntelligence (AI) applications are rapidly expanding worldwide, and have the potential to impact to all areas of medical practice. Medical imaging applications constitute a vast majority of approved clinical AI applications. Thoug
Externí odkaz:
http://arxiv.org/abs/2202.02833
Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There have been several proposals for efficient and robust visual representation learning among vision research practi
Externí odkaz:
http://arxiv.org/abs/2112.13734
Autor:
Cohen, Joseph Paul, Viviano, Joseph D., Bertin, Paul, Morrison, Paul, Torabian, Parsa, Guarrera, Matteo, Lungren, Matthew P, Chaudhari, Akshay, Brooks, Rupert, Hashir, Mohammad, Bertrand, Hadrien
TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. It provides a common interface and common pre-processing chain for a wide set of publicly available chest X-ray datasets. In addition,
Externí odkaz:
http://arxiv.org/abs/2111.00595
Autor:
Lemay, Andreanne, Gros, Charley, Vincent, Olivier, Liu, Yaou, Cohen, Joseph Paul, Cohen-Adad, Julien
Medical images are often accompanied by metadata describing the image (vendor, acquisition parameters) and the patient (disease type or severity, demographics, genomics). This metadata is usually disregarded by image segmentation methods. In this wor
Externí odkaz:
http://arxiv.org/abs/2102.09582
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