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Feasibility of Quantifying Amyloid Burden Using Volumetric MRI Data: Preliminary Findings Based on the Deep Learning 3D Convolutional Neural Network Approach

Published on Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2018

Recommended citation: Ye Yuan et al. (2018). "Feasibility of Quantifying Amyloid Burden Using Volumetric MRI Data: Preliminary Findings Based on the Deep Learning 3D Convolutional Neural Network Approach." Alzheimer's & Dementia: The Journal of the Alzheimer's Association. Volume 14, Issue 7, P30 - P31. [link]

Quantification of Amyloid Burden from Florbetapir PET Images without Using Target and Reference Regions: Preliminary Findings Based on the Deep Learning 3D Convolutional Neural Network Approach

Published on Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2018

Recommended citation: Ye Yuan et al. (2018). "Quantification of Amyloid Burden from Florbetapir PET Images without Using Target and Reference Regions: Preliminary Findings Based on the Deep Learning 3D Convolutional Neural Network Approach." Alzheimer's & Dementia: The Journal of the Alzheimer's Association. Volume 14, Issue 7, P31. [link]

Bridging the Gap Between Computational Photography and Visual Recognition

Published on Arxiv Preprint, 2019

In this paper, we introduced the UG2 dataset as a large-scale benchmark composed of video imagery captured under challenging conditions, and two enhancement tasks designed to test algorithmic impact on visual quality and automatic object recognition. Furthermore, we proposed a set of metrics to evaluate the joint improvement of such tasks as well as individual algorithmic advances, including a novel psychophysics-based evaluation regime for human assessment and a realistic set of quantitative measures for object recognition performance. We introduced six new algorithms for image restoration or enhancement, which were created as part of the IARPA sponsored UG2 Challenge workshop held at CVPR 2018.

Recommended citation: Rosaura G. VidalMata et al. (2019). "Bridging the Gap Between Computational Photography and Visual Recognition" Arxiv Preprint. 1901.09482. [link]

UG2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments

Published on Arxiv Preprint, 2019

A summary paper on datasets, fact sheets and baseline results in UG2+ Challenge (Track 2). More materials are provided in http://www.ug2challenge.org.

Recommended citation: Ye Yuan et al. (2019). "UG2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments" Arxiv Preprint. 1904.04474. [link]

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