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Latent Feature Representation via Unsupervised Learning for Pattern Discovery in Massive Electron Microscopy Image Volumes

Autor: Huang, Gary B, Yang, Huei-Fang, Takemura, Shin-ya, Rivlin, Pat, Plaza, Stephen M

We propose a method to facilitate exploration and analysis of new large data sets. In particular, we give an unsupervised deep learning approach to learning a latent representation that captures semantic similarity in the data set. The core idea is t

Externí odkaz: http://arxiv.org/abs/2012.12175

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Akademický článek

Associations between retinol-binding protein 4 and cardiometabolic risk factors and subclinical atherosclerosis in recently postmenopausal women: cross-sectional analyses from the KEEPS study

Autor: Huang Gary, Wang Dan, Khan Unab I, Zeb Irfan, Manson JoAnn E, Miller Virginia, Hodis Howard N, Budoff Matthew J, Merriam George R, Harman Mitchell S, Brinton Eliot A, Cedars Marcelle I, Su Yali, Lobo Rogerio A, Naftolin Frederick, Santoro Nanette, Taylor Hugh S, Wildman Rachel P

Publikováno v: Cardiovascular Diabetology, Vol 11, Iss 1, p 52 (2012)

Abstract Background The published literature regarding the relationships between retinol-binding protein 4 (RBP4) and cardiometabolic risk factors and subclinical atherosclerosis is conflicting, likely due, in part, to limitations of frequently used

Externí odkaz: https://doaj.org/article/3735d1df1a6b4c2ba09d7801f4ce40d6

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Akademický článek

Telemedicine vs Face-to-Face for Nursing Home Residents With Acute Presentations: A Noninferiority Study

Autor: Huang, Gary Y., Kumar, Manoj, Liu, Xinsheng, Irwanto, Deni, Zhou, You, Chirapa, Ethel, Xu, Ying H., Shulruf, Boaz, Chan, Daniel K.Y.

Publikováno v: In Journal of the American Medical Directors Association October 2023 24(10):1471-1477

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Fully-Automatic Synapse Prediction and Validation on a Large Data Set

Autor: Huang, Gary B., Scheffer, Louis K., Plaza, Stephen M.

Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of synapses between neurons. As manual extraction of this information is very time-consuming, there has been extensive research eff

Externí odkaz: http://arxiv.org/abs/1604.03075

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Large-scale EM Analysis of the Drosophila Antennal Lobe with Automatically Computed Synapse Point Clouds

Autor: Zhao, Ting, Takemura, Shin-ya, Huang, Gary B., Horne, Jane Anne, Katz, William T., Shinomiya, Kazunori, Scheffer, Louis K., Meinertzhagen, Ian A., Rivlin, Patricia K., Plaza, Stephen M.

The promise of extracting connectomes and performing useful analysis on large electron microscopy (EM) datasets has been an elusive dream for many years. Tracing in even the smallest portions of neuropil requires copious human annotation, the rate-li

Externí odkaz: http://arxiv.org/abs/1508.06232

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Identifying Synapses Using Deep and Wide Multiscale Recursive Networks

Autor: Huang, Gary B., Plaza, Stephen

In this work, we propose a learning framework for identifying synapses using a deep and wide multi-scale recursive (DAWMR) network, previously considered in image segmentation applications. We apply this approach on electron microscopy data from inve

Externí odkaz: http://arxiv.org/abs/1409.1789

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