Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Bhaskhar, Nandita"'
Autor:
van der Sluijs, Rogier, Bhaskhar, Nandita, Rubin, Daniel, Langlotz, Curtis, Chaudhari, Akshay
Publikováno v:
Proceedings of Machine Learning Research, MIDL 2023
Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques. While augmentation strategies for natural imaging have been studied extensively, medical images are vastly different from
Externí odkaz:
http://arxiv.org/abs/2301.12636
Autor:
Dominic, Jeffrey, Bhaskhar, Nandita, Desai, Arjun D., Schmidt, Andrew, Rubin, Elka, Gunel, Beliz, Gold, Garry E., Hargreaves, Brian A., Lenchik, Leon, Boutin, Robert, Chaudhari, Akshay S.
Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medical imaging field. Self-supervised learning (SSL) methods involving pre
Externí odkaz:
http://arxiv.org/abs/2210.07936
Publikováno v:
IEEE Transactions on Artificial Intelligence, 2023
Continuous monitoring of trained ML models to determine when their predictions should and should not be trusted is essential for their safe deployment. Such a framework ought to be high-performing, explainable, post-hoc and actionable. We propose TRU
Externí odkaz:
http://arxiv.org/abs/2207.11290
Autor:
Dubost, Florian, Hong, Erin, Pike, Max, Sharma, Siddharth, Tang, Siyi, Bhaskhar, Nandita, Lee-Messer, Christopher, Rubin, Daniel
Optimization plays a key role in the training of deep neural networks. Deciding when to stop training can have a substantial impact on the performance of the network during inference. Under certain conditions, the generalization error can display a d
Externí odkaz:
http://arxiv.org/abs/2106.02100
Autor:
Dubost, Florian, Hong, Erin, Bhaskhar, Nandita, Tang, Siyi, Rubin, Daniel, Lee-Messer, Christopher
Publikováno v:
In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023
Labeled data is a critical resource for training and evaluating machine learning models. However, many real-life datasets are only partially labeled. We propose a semi-supervised machine learning training strategy to improve event detection performan
Externí odkaz:
http://arxiv.org/abs/2011.14101
Publikováno v:
In Journal of Biomedical Informatics November 2023 147
Autor:
Dominic, Jeffrey1 (AUTHOR), Bhaskhar, Nandita2 (AUTHOR) nanbhas@stanford.edu, Desai, Arjun D.1,2 (AUTHOR), Schmidt, Andrew1 (AUTHOR), Rubin, Elka1 (AUTHOR), Gunel, Beliz2 (AUTHOR), Gold, Garry E.1 (AUTHOR), Hargreaves, Brian A.1,2,3 (AUTHOR), Lenchik, Leon4 (AUTHOR), Boutin, Robert1 (AUTHOR), Chaudhari, Akshay S.1,5,6 (AUTHOR)
Publikováno v:
Bioengineering (Basel). Feb2023, Vol. 10 Issue 2, p207. 22p.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.