Zobrazeno 1 - 8
of 8
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
Models driven by spurious correlations often yield poor generalization performance. We propose the counterfactual alignment method to detect and explore spurious correlations of black box classifiers. Counterfactual images generated with respect to o
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:
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:
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
Autor:
Bertin, Paul, Hashir, Mohammad, Weiss, Martin, Frappier, Vincent, Perkins, Theodore J., Boucher, Geneviève, Cohen, Joseph Paul
Gene interaction graphs aim to capture various relationships between genes and can represent decades of biology research. When trying to make predictions from genomic data, those graphs could be used to overcome the curse of dimensionality by making
Externí odkaz:
http://arxiv.org/abs/1905.02295
The number of visually impaired or blind (VIB) people in the world is estimated at several hundred million. Based on a series of interviews with the VIB and developers of assistive technology, this paper provides a survey of machine-learning based mo
Externí odkaz:
http://arxiv.org/abs/1811.10120
Autor:
Luck, Margaux, Sylvain, Tristan, Cohen, Joseph Paul, Cardinal, Heloise, Lodi, Andrea, Bengio, Yoshua
Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored. Utilizing this information is a challenge because it is not obvious how to correctly incorporate these censored examples
Externí odkaz:
http://arxiv.org/abs/1806.01984