P1-118: QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Ye Yuan; Zhangyang Wang; Wendy Lee; Paul VanGilder; Yinghua Chen; Eric M. Reiman; Kewei Chen

doi:10.1016/j.jalz.2018.06.121

P1-118: QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.121 ◽

2006 ◽

Vol 14 (7S_Part_5) ◽

pp. P315-P316 ◽

Cited By ~ 1

Author(s):

Ye Yuan ◽

Zhangyang Wang ◽

Wendy Lee ◽

Paul VanGilder ◽

Yinghua Chen ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Amyloid Burden ◽

Neural Network Approach

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IC-P-025: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.2089 ◽

2006 ◽

Vol 14 (7S_Part_1) ◽

pp. P30-P31

Author(s):

Ye Yuan ◽

Zhangyang Wang ◽

Wendy Lee ◽

Pradeep Thiyyagura ◽

Eric M. Reiman ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Amyloid Burden ◽

Volumetric Mri ◽

Neural Network Approach

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IC-P-026: 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 ◽

10.1016/j.jalz.2018.06.2090 ◽

2006 ◽

Vol 14 (7S_Part_1) ◽

pp. P31-P31

Author(s):

Ye Yuan ◽

Zhangyang Wang ◽

Wendy Lee ◽

Paul VanGilder ◽

Yinghua Chen ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Amyloid Burden ◽

Neural Network Approach

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P2-073: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.758 ◽

2006 ◽

Vol 14 (7S_Part_12) ◽

pp. P695-P695

Author(s):

Ye Yuan ◽

Zhangyang Wang ◽

Wendy Lee ◽

Pradeep Thiyyagura ◽

Eric M. Reiman ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Amyloid Burden ◽

Volumetric Mri ◽

Neural Network Approach

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Fruit Classification Through Deep Learning: A Convolutional Neural Network Approach

Lecture Notes in Electrical Engineering - Communications, Signal Processing, and Systems ◽

10.1007/978-981-13-9409-6_326 ◽

2020 ◽

pp. 2671-2677

Author(s):

Tahir Arshad ◽

Min Jia ◽

Qing Guo ◽

Xuemai Gu ◽

Xiaofeng Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Neural Network Approach

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A Detailed Study of Deep Learning using Convolutional Neural Network Approach

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2020.32306 ◽

2020 ◽

Vol 8 (11) ◽

pp. 831-837

Author(s):

Parkavi J.

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Neural Network Approach

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Detecting diseases in plant leaves: an optimised deep-learning convolutional neural network approach

International Journal of Environment and Sustainable Development ◽

10.1504/ijesd.2021.114562 ◽

2021 ◽

Vol 20 (2) ◽

pp. 166

Author(s):

Saraansh Baranwal ◽

Anuja Arora ◽

Siddhant Khandelwal

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Plant Leaves ◽

Neural Network Approach

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Detecting diseases in plant leaves: an optimised deep-learning convolutional neural network approach

International Journal of Environment and Sustainable Development ◽

10.1504/ijesd.2021.10036707 ◽

2021 ◽

Vol 20 (2) ◽

pp. 166

Author(s):

Siddhant Khandelwal ◽

Anuja Arora ◽

Saraansh Baranwal

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Plant Leaves ◽

Neural Network Approach

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Using deep learning with convolutional neural network approach to identify the invasion depth of endometrial cancer in myometrium using MR images: A pilot study

Gynecologic Oncology ◽

10.1016/j.ygyno.2020.05.409 ◽

2020 ◽

Vol 159 ◽

pp. 240-241

Author(s):

C.C. Chang

Keyword(s):

Neural Network ◽

Endometrial Cancer ◽

Deep Learning ◽

Pilot Study ◽

Convolutional Neural Network ◽

Network Approach ◽

Mr Images ◽

Invasion Depth ◽

Neural Network Approach

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Estimating ambient visibility in the presence of fog: a deep convolutional neural network approach

Personal and Ubiquitous Computing ◽

10.1007/s00779-019-01334-w ◽

2019 ◽

Cited By ~ 1

Author(s):

Fatma Outay ◽

Bilal Taha ◽

Hazar Chaabani ◽

Faouzi Kamoun ◽

Naoufel Werghi ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network ◽

Network Approach ◽

Neural Network Approach

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Packet Vision: a convolutional neural network approach for network traffic classification

2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) ◽

10.1109/sibgrapi51738.2020.00042 ◽

2020 ◽

Author(s):

Rodrigo Moreira ◽

Larissa Ferreira Rodrigues ◽

Pedro Frosi Rosa ◽

Rui L. Aguiar ◽

Flavio de Oliveira Silva

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Traffic ◽

Traffic Classification ◽

Network Approach ◽

Neural Network Approach ◽

Network Traffic Classification

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