Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Dimah Dera"'
Publikováno v:
Frontiers in Medical Technology, Vol 4 (2022)
Deep neural networks (DNNs) have started to find their role in the modern healthcare system. DNNs are being developed for diagnosis, prognosis, treatment planning, and outcome prediction for various diseases. With the increasing number of application
Externí odkaz:
https://doaj.org/article/04a066502b474ecdb051c5dbe83607b5
Publikováno v:
IEEE Transactions on Signal Processing. 69:4669-4684
Deep neural networks (DNNs) have surpassed human-level accuracy in various learning tasks. However, unlike humans who have a natural cognitive intuition for probabilities, DNNs cannot express their uncertainty in the output decisions. This limits the
Publikováno v:
MLSP
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs). We propose a new theoretically grounded and efficient approach for robust learning that builds upon Bayesian estimation and Variational I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04c936e66f463c759d082155211049dc
With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72bbd86d230150a84f00aaa732cf28e1
Publikováno v:
Deep Learning for Biomedical Data Analysis ISBN: 9783030716752
Brain tumor segmentation refers to the process of pixel-level delineation of brain tumor structures in medical images, such as Magnetic Resonance Imaging (MRI). Brain tumor segmentation is required for radiotherapy treatment planning and can diagnosi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::589b5f96f2c7a4e1f22054a553074dd3
https://doi.org/10.1007/978-3-030-71676-9_13
https://doi.org/10.1007/978-3-030-71676-9_13
Publikováno v:
MLSP
Machine learning models have achieved human-level performance on various tasks. This success comes at a high cost of computation and storage overhead, which makes machine learning algorithms difficult to deploy on edge devices. Typically, one has to
Autor:
Nidhal Bouaynaya, Adam Eichen, Dimah Dera, Stephen Shanko, Sanipa Arnold, Ghulam Rasool, Jeff Cammerata
Publikováno v:
2020 IEEE International Radar Conference (RADAR).
Synthetic aperture radar (SAR) image classification is a challenging problem due to the complex imaging mechanism as well as the random speckle noise, which affects radar image interpretation. Recently, convolutional neural networks (CNNs) have been
Publikováno v:
MLSP
Model confidence or uncertainty is critical in autonomous systems as they directly tie to the safety and trustworthiness of the system. The quantification of uncertainty in the output decisions of deep neural networks (DNNs) is a challenging problem.
Publikováno v:
IJCNN
Data plenitude is the bottleneck for data-driven approaches, including neural networks. In particular, Convolutional Neural Networks (CNNs) require an abundant database of training images to achieve a desired high accuracy. Current techniques employe
The primary goal of this chapter is to provide a basic understanding of the machine learning methods for transportation-related applications. This chapter discusses how the machine learning methods can be utilized to improve performance of transporta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e14cc2df0f44ab4ab3c35d933a4eb1b4
https://doi.org/10.1016/b978-0-12-809715-1.00012-2
https://doi.org/10.1016/b978-0-12-809715-1.00012-2