Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Dunnmon, P."'
Autor:
Tang, Siyi, Dunnmon, Jared A., Qu, Liangqiong, Saab, Khaled K., Baykaner, Tina, Lee-Messer, Christopher, Rubin, Daniel L.
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in multivariate biosignals is challenging due to (1) long-range temporal dep
Externí odkaz:
http://arxiv.org/abs/2211.11176
Autor:
Paolo, Fernando, Lin, Tsu-ting Tim, Gupta, Ritwik, Goodman, Bryce, Patel, Nirav, Kuster, Daniel, Kroodsma, David, Dunnmon, Jared
Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that do not show up in conventional monitoring systems -- known as ``dark vessels'' -- is key to managing and securing the health of
Externí odkaz:
http://arxiv.org/abs/2206.00897
Autor:
Tang, Siyi, Tariq, Amara, Dunnmon, Jared, Sharma, Umesh, Elugunti, Praneetha, Rubin, Daniel, Patel, Bhavik N., Banerjee, Imon
Publikováno v:
IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 4, pp. 2071-2082, April 2023
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged. While recent deep learning-based
Externí odkaz:
http://arxiv.org/abs/2204.06766
Autor:
Eyuboglu, Sabri, Varma, Maya, Saab, Khaled, Delbrouck, Jean-Benoit, Lee-Messer, Christopher, Dunnmon, Jared, Zou, James, Ré, Christopher
Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e.g. images, a
Externí odkaz:
http://arxiv.org/abs/2203.14960
Autor:
Joshi, Anirudh, Eyuboglu, Sabri, Huang, Shih-Cheng, Dunnmon, Jared, Soin, Arjun, Davidzon, Guido, Chaudhari, Akshay, Lungren, Matthew P
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy. Approaches to automate longtudinal analysis present many challenges including lac
Externí odkaz:
http://arxiv.org/abs/2108.02016
Autor:
Tang, Siyi, Dunnmon, Jared A., Saab, Khaled, Zhang, Xuan, Huang, Qianying, Dubost, Florian, Rubin, Daniel L., Lee-Messer, Christopher
Publikováno v:
ICLR 2022
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment. However, several modeling challenges remain unaddressed in prior automated seizure detection and classification stud
Externí odkaz:
http://arxiv.org/abs/2104.08336
In real-world classification tasks, each class often comprises multiple finer-grained "subclasses." As the subclass labels are frequently unavailable, models trained using only the coarser-grained class labels often exhibit highly variable performanc
Externí odkaz:
http://arxiv.org/abs/2011.12945
Autor:
Tang, Siyi, Ghorbani, Amirata, Yamashita, Rikiya, Rehman, Sameer, Dunnmon, Jared A., Zou, James, Rubin, Daniel L.
The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may
Externí odkaz:
http://arxiv.org/abs/2010.08006
Autor:
Kuang, Zhaobin, Sala, Frederic, Sohoni, Nimit, Wu, Sen, Córdova-Palomera, Aldo, Dunnmon, Jared, Priest, James, Ré, Christopher
A popular way to estimate the causal effect of a variable x on y from observational data is to use an instrumental variable (IV): a third variable z that affects y only through x. The more strongly z is associated with x, the more reliable the estima
Externí odkaz:
http://arxiv.org/abs/2004.05316
Autor:
Hooper, Sarah M., Dunnmon, Jared A., Lungren, Matthew P., Gambhir, Sanjiv Sam, Ré, Christopher, Wang, Adam S., Patel, Bhavik N.
Automated medical image classification with convolutional neural networks (CNNs) has great potential to impact healthcare, particularly in resource-constrained healthcare systems where fewer trained radiologists are available. However, little is know
Externí odkaz:
http://arxiv.org/abs/2003.07977