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Deep Residual Nets for Improved Alzheimer's Diagnosis

Published: 20 August 2017 Publication History

Abstract

We propose a framework that leverages deep residual CNNs pretrained on large, non-biomedical image data sets. These pretrained networks learn cross-domain features that improve low-level interpretation of images. We evaluate our model on brain imaging data and show that pretraining and the use of deep residual networks are crucial to seeing large improvements in Alzheimer's Disease diagnosis from brain MRIs.

References

[1]
Ashish Gupta, Murat Ayhan, and Anthony Maida. 2013. Natural Image Bases to Represent Neuroimaging Data Proceedings of the 30th International Conference on Machine Learning. Atlanta, Georgia, USA, 987--994.
[2]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. (2015).

Cited By

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  • (2025)Temporal Graphormer and its interpretability: A novel framework for diagnostic decoding of brain disorders using fMRI dataBiomedical Signal Processing and Control10.1016/j.bspc.2024.107467104(107467)Online publication date: Jun-2025
  • (2024)New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage ClassificationAI10.3390/ai50100175:1(342-363)Online publication date: 1-Feb-2024
  • (2024)Spatial attention enhanced deep learning model for Alzheimer’s disease diagnosis2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10725271(1-5)Online publication date: 24-Jun-2024
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cover image ACM Conferences
ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics
August 2017
800 pages
ISBN:9781450347228
DOI:10.1145/3107411
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2017

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Author Tags

  1. Alzheimer's disease
  2. convolutional neural networks
  3. image analysis

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BCB '17
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ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;
Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

View all
  • (2025)Temporal Graphormer and its interpretability: A novel framework for diagnostic decoding of brain disorders using fMRI dataBiomedical Signal Processing and Control10.1016/j.bspc.2024.107467104(107467)Online publication date: Jun-2025
  • (2024)New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage ClassificationAI10.3390/ai50100175:1(342-363)Online publication date: 1-Feb-2024
  • (2024)Spatial attention enhanced deep learning model for Alzheimer’s disease diagnosis2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10725271(1-5)Online publication date: 24-Jun-2024
  • (2024)Multimodal 3D Deep Learning for Early Diagnosis of Alzheimer’s DiseaseIEEE Access10.1109/ACCESS.2024.338186212(46278-46289)Online publication date: 2024
  • (2024)Joint transformer architecture in brain 3D MRI classification: its application in Alzheimer’s disease classificationScientific Reports10.1038/s41598-024-59578-314:1Online publication date: 18-Apr-2024
  • (2024)Alzheimer’s disease diagnosis using deep learning techniques: datasets, challenges, research gaps and future directionsInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02441-5Online publication date: 30-Jul-2024
  • (2024)Early detection of Alzheimer’s disease using squeeze and excitation network with local binary pattern descriptorPattern Analysis and Applications10.1007/s10044-024-01280-127:2Online publication date: 9-May-2024
  • (2023)Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational AutoencoderIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.318577345:3(2879-2896)Online publication date: 1-Mar-2023
  • (2023)Graph Reasoning Module for Alzheimer’s Disease Diagnosis: A Plug-and-Play MethodIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2023.333753331(4773-4780)Online publication date: 2023
  • (2023)BGL-Net: A Brain-Inspired Global-Local Information Fusion Network for Alzheimer’s Disease Based on sMRIIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2022.320478215:3(1161-1169)Online publication date: Sep-2023
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