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neural networks (4) 4
alzheimer's disease (3) 3
classification (2) 2
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computer science, artificial intelligence (2) 2
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1. Full Text Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network
by Lu, Donghuan and Heisler, Morgan and Lee, Sieun and Ding, Gavin Weiguang and Navajas, Eduardo and Sarunic, Marinko V and Beg, Mirza Faisal
Medical Image Analysis, ISSN 1361-8415, 05/2019, Volume 54, pp. 100 - 110
As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. These images can help... 
Fully convolutional network | Optical coherence tomography | Multiclass segmentation and detection | Retinal fluid | DIABETIC-RETINOPATHY | ENGINEERING, BIOMEDICAL | QUANTIFICATION | ALGORITHM | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | DETACHMENT SEGMENTATION | SD-OCT | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | AUTOMATIC SEGMENTATION | MACULAR EDEMA | LAYER | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Tomography | Algorithms | Neural networks | Medical imaging equipment | Edema | Image segmentation | Medical imaging | Optical Coherence Tomography | Segmentation | Image processing | Artificial neural networks | Retina | Image detection
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2. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease
by Donghuan Lu and Karteek Popuri and Gavin Weiguang Ding and Rakesh Balachandar and Mirza Faisal Beg
Medical Image Analysis, ISSN 1361-8415, 05/2018, Volume 46, p. 26
Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory... 
Neuroimaging | Brain | Classifiers | Medical imaging | Neurodegenerative diseases | Memory | Cognitive ability | Medical services | Positron emission | Impairment | Glucose | Metabolism | Neurological diseases | Learning | Neural networks | Multiscale analysis | Tomography | Dementia disorders | Alzheimers disease | Positron emission tomography | Alzheimer's disease
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3. Full Text Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease
by Lu, Donghuan and Popuri, Karteek and Ding, Gavin Weiguang and Balachandar, Rakesh and Beg, Mirza Faisal
Medical Image Analysis, ISSN 1361-8415, 05/2018, Volume 46, pp. 26 - 34
Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory... 
Early diagnosis | Multiscale deep neural network learning | Alzheimer’s disease | Metabolism FDG-PET
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4. Full Text Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images
by Lu, DH and Popuri, K and Ding, GW and Balachandar, R and Beg, MF and Alzheimer's Dis Neuroimaging Initi and Alzheimer’s Disease Neuroimaging Initiative
SCIENTIFIC REPORTS, ISSN 2045-2322, 04/2018, Volume 8, Issue 1, p. 5697
Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective... 
CONVERSION | MULTIDISCIPLINARY SCIENCES | REPRESENTATION | CLASSIFICATION | PATTERNS | MILD COGNITIVE IMPAIRMENT | PREDICTION | FEATURES
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5. Full Text Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease
by Lu, Donghuan and Popuri, Karteek and Ding, Gavin Weiguang and Balachandar, Rakesh and Beg, Mirza Faisal and Alzheimers Dis Neuroimaging Initia and Alzheimer’s Disease Neuroimaging Initiative
Medical Image Analysis, ISSN 1361-8415, 05/2018, Volume 46, pp. 26 - 34
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory... 
Early diagnosis | Multiscale deep neural network learning | Alzheimer's disease | Metabolism FDG-PET | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | CONVERSION | ENGINEERING, BIOMEDICAL | OPTIMIZATION | MILD COGNITIVE IMPAIRMENT | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | FEATURES | Humans | Female | Fluorodeoxyglucose F18 | Male | Aged | Alzheimer Disease - diagnostic imaging | Positron-Emission Tomography - methods | Radiopharmaceuticals | Early Diagnosis | Neural Networks (Computer) | Physiological aspects | Self-care, Health | PET imaging | Neural networks
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6. Full Text Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images
by Lu, Donghuan and Popuri, Karteek and Ding, Gavin Weiguang and Balachandar, Rakesh and Beg, Mirza Faisal and Weiner, Michael and Aisen, Paul and Petersen, Ronald and Jack, Cliford and Jagust, William and Trojanowki, John and Toga, Arthur and Beckett, Laurel and Green, Robert and Saykin, Andrew and Morris, John and Shaw, Leslie and Kaye, Jefrey and Quinn, Joseph and Silbert, Lisa and Lind, Betty and Carter, Raina and Dolen, Sara and Schneider, Lon and Pawluczyk, Sonia and Beccera, Mauricio and Teodoro, Liberty and Spann, Bryan and Brewer, James and Vanderswag, Helen and Fleisher, Adam and Heidebrink, Judith and Lord, Joanne and Mason, Sara and Albers, Colleen and Knopman, David and Johnson, Kris and Doody, Rachelle and Villanueva-Meyer, Javier and Chowdhury, Munir and Rountree, Susan and Dang, Mimi and Stern, Yaakov and Honig, Lawrence and Bell, Karen and Ances, Beau and Carroll, Maria and Creech, Mary and Franklin, Erin and Mintun, Mark and Schneider, Stacy and Oliver, Angela and Marson, Daniel and Grifth, Randall and Clark, David and Geldmacher, David and Brockington, John and Roberson, Erik and Love, Marissa Natelson and Grossman, Hillel and Mitsis, Efe and Shah, Raj and deToledo-Morrell, Leyla and Duara, Ranjan and Varon, Daniel and Greig, Maria and Roberts, Peggy and Albert, Marilyn and Onyike, Chiadi and D’Agostino, Daniel and Kielb, Stephanie and Galvin, James and Cerbone, Brittany and Michel, Christina and Pogorelec, Dana and Rusinek, Henry and de Leon, Mony and Glodzik, Lidia and Santi, Susan De and Doraiswamy, P and Petrella, Jefrey and Borges-Neto, Salvador and Wong, Terence and Coleman, Edward and Smith, Charles and Jicha, Greg and Hardy, Peter and Sinha, Partha and Oates, Elizabeth and Conrad, Gary and Porsteinsson, Anton and Goldstein, Bonnie and Martin, Kim and Makino, Kelly and Ismail, M and Brand, Connie and Mulnard, Ruth and Thai, Gaby and Mc-Adams-Ortiz, Catherine and Womack, Kyle and ... and Alzheimer’s Disease Neuroimaging Initiative
Scientific Reports, ISSN 2045-2322, 12/2018, Volume 8, Issue 1, pp. 1 - 13
Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective... 
Neuroimaging | Glucose metabolism | Magnetic resonance imaging | Neural networks | Cognitive ability | Biomarkers | Diagnosis | Metabolism | Alzheimer's disease | Positron emission tomography | Structure-function relationships
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7. Full Text On the Effectiveness of Low Frequency Perturbations
by Sharma, Yash and Ding, Gavin Weiguang and Brubaker, Marcus
02/2019
Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs,... 
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8. Full Text advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
by Ding, Gavin Weiguang and Wang, Luyu and Jin, Xiaomeng
02/2019
advertorch is a toolbox for adversarial robustness research. It contains various implementations for attacks, defenses and robust training methods. advertorch... 
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9. Full Text On the Sensitivity of Adversarial Robustness to Input Data Distributions
by Ding, Gavin Weiguang and Lui, Kry Yik Chau and Jin, Xiaomeng and Wang, Luyu and Huang, Ruitong
02/2019
Neural networks are vulnerable to small adversarial perturbations. Existing literature largely focused on understanding and mitigating the vulnerability of... 
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10. Full Text Max-Margin Adversarial (MMA) Training: Direct Input Space Margin Maximization through Adversarial Training
by Ding, Gavin Weiguang and Sharma, Yash and Lui, Kry Yik Chau and Huang, Ruitong
12/2018
We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a... 
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11. Full Text Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds
by Lui, Kry Yik Chau and Ding, Gavin Weiguang and Huang, Ruitong and McCann, Robert J
10/2018
Neural Information Processing Systems (NIPS 2018) In this paper, we investigate Dimensionality reduction (DR) maps in an information retrieval setting from a... 
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12. Full Text Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
by Cao, Yanshuai and Ding, Gavin Weiguang and Lui, Kry Yik-Chau and Huang, Ruitong
05/2018
We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs). The motivation is that when the discriminator D spreads out... 
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