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pro vyhledávání: '"Brown, James M."'
Imaging mass cytometry (IMC) is a relatively new technique for imaging biological tissue at subcellular resolution. In recent years, learning-based segmentation methods have enabled precise quantification of cell type and morphology, but typically re
Externí odkaz:
http://arxiv.org/abs/2402.04446
Open space grassland is being increasingly farmed or built upon, leading to a ramping up of conservation efforts targeting roadside verges. Approximately half of all UK grassland species can be found along the country's 500,000 km of roads, with some
Externí odkaz:
http://arxiv.org/abs/2206.04271
Autor:
Nwokedi, Ezechukwu I., Bains, Rasneer S., Bidaut, Luc, Ye, Xujiong, Wells, Sara, Brown, James M.
This paper presents a spatiotemporal deep learning approach for mouse behavioural classification in the home-cage. Using a series of dual-stream architectures with assorted modifications to increase performance, we introduce a novel feature sharing a
Externí odkaz:
http://arxiv.org/abs/2206.00614
Autor:
Coyner, Aaron S., Singh, Praveer, Brown, James M., Ostmo, Susan, Chan, R. V. Paul, Chiang, Michael F., Kalpathy-Cramer, Jayashree, Campbell, J. Peter
Background: Artificial intelligence (AI) may demonstrate racial bias when skin or choroidal pigmentation is present in medical images. Recent studies have shown that convolutional neural networks (CNNs) can predict race from images that were not prev
Externí odkaz:
http://arxiv.org/abs/2109.13845
Autor:
Nwokedi, Ezechukwu I, Bains, Rasneer S, Bidaut, Luc, Wells, Sara, Ye, Xujiong, Brown, James M
This paper explores the application of unsupervised learning to detecting anomalies in mouse video data. The two models presented in this paper are a dual-stream, 3D convolutional autoencoder (with residual connections) and a dual-stream, 2D convolut
Externí odkaz:
http://arxiv.org/abs/2106.00598
Akademický článek
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Autor:
Chao, Jin, Badawi, Ahmad Al, Unnikrishnan, Balagopal, Lin, Jie, Mun, Chan Fook, Brown, James M., Campbell, J. Peter, Chiang, Michael, Kalpathy-Cramer, Jayashree, Chandrasekhar, Vijay Ramaseshan, Krishnaswamy, Pavitra, Aung, Khin Mi Mi
Convolutional neural networks (CNNs) have enabled significant performance leaps in medical image classification tasks. However, translating neural network models for clinical applications remains challenging due to data privacy issues. Fully Homomorp
Externí odkaz:
http://arxiv.org/abs/1901.10074
Autor:
Perrett, Andrew, Pollard, Harry, Barnes, Charlie, Schofield, Mark, Qie, Lan, Bosilj, Petra, Brown, James M.
Publikováno v:
In Computers, Environment and Urban Systems June 2023 102
Autor:
Lecouat, Bruno, Chang, Ken, Foo, Chuan-Sheng, Unnikrishnan, Balagopal, Brown, James M., Zenati, Houssam, Beers, Andrew, Chandrasekhar, Vijay, Kalpathy-Cramer, Jayashree, Krishnaswamy, Pavitra
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require resource-intensive expert annotation. Semi-supervised generative adver
Externí odkaz:
http://arxiv.org/abs/1812.07832
Autor:
Hale, Ralph G.1 (AUTHOR) ralph.hale@ung.edu, Brown, James M.2 (AUTHOR)
Publikováno v:
Perception. Feb2024, Vol. 53 Issue 2, p110-124. 15p.