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Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease
[article]
2021
arXiv
pre-print
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In this work, we propose three explainable deep learning architectures to automatically detect patients with Alzheimer's disease based on their language abilities. ...
We use self-attention mechanisms and interpretable 1-dimensional ConvolutionalNeural Network (CNN) to generate two types of explanations of the model's action: intra-class explanation and inter-class explanation ...
Conclusion We proposed three explainable architectures using CNN and attention to detect Alzheimer's disease using two kinds of features: part-of-speech and language embeddings. ...
arXiv:2006.14135v2
fatcat:xzdb42eabzfwpnqks6vw54iu4u
Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease
[article]
2020
medRxiv
pre-print
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In this work we propose three explainable deep learning architectures to automatically detect patients with Alzheimer's disease based on their language abilities. ...
We use self-attention mechanisms and interpretable 1-dimensional Convolutional Neural Network (CNN) to generate two types of explanations of the model's action: intra-class explanation and inter-class ...
Conclusion We proposed three explainable architectures using CNN and attention to detect Alzheimer's disease using two kinds of features: part-of-speech and language embeddings. ...
doi:10.1101/2020.06.24.20139592
fatcat:uh7qq7fc4zcu7fblttt2bnjuny
An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI
[article]
2020
arXiv
pre-print
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In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from ...
method; 3) This work has provided a full end-to-end learning solution for automated disease diagnosis. ...
For the explainable model evaluation, the 3D Grad-CAM has been used to explain the model for Alzheimer's Disease Diagnosis. ...
arXiv:2008.04024v1
fatcat:3g3j3qgkqfdchabdpymv6yale4
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification
2018
AMIA Annual Symposium Proceedings
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We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. ...
The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives. ...
Introduction For years, medical informatics researchers have pursued data-driven methods to automate disease diagnosis procedures for early detection of many deadly diseases. ...
pmid:30815203
pmcid:PMC6371279
fatcat:3tyqplujlnfqpdahs5ofk4irhq
Stacked Deep Dense Neural Network Model to Predict Alzheimer's Dementia Using Audio Transcript Data
2022
IEEE Access
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Network (SDDNN) model for text classification and prediction of Alzheimer's dementia. ...
This study involves the use of Convolutional Neural Network (CNN), designed a hybrid model with CNN & Bidirectional Long-Short Term Memory (Bidirectional LSTM), and proposed a Stacked Deep Dense Neural ...
C. CONFLICTS OF INTEREST The authors declare that they have no conflicts of interest to report regarding the present study. ...
doi:10.1109/access.2022.3161749
fatcat:mz3avhjztjdo3ojlnp2qsw5voa
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification
[article]
2018
arXiv
pre-print
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We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. ...
The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives. ...
Introduction For years, medical informatics researchers have pursued data-driven methods to automate disease diagnosis procedures for early detection of many deadly diseases. ...
arXiv:1803.02544v3
fatcat:og3h2kfqurdkrjpssntumz7ev4
2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24
2020
IEEE journal of biomedical and health informatics
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., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan ...
. 2020 319-329 Hicks, Y.A., see Azzopardi, C., 1004-1015 Hidalgo, A.S., see Hamad, R.A., JBHI Feb. 2020 387-395 Hill, C.E., see 2389-2397 Hirano, Y., see Xu, R., 2041-2052 Hocevar, A., see Vukicevic ...
., +, JBHI Dec. 2020 3491-3498 Thorax-Net: An Attention Regularized Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography. ...
doi:10.1109/jbhi.2020.3048808
fatcat:iifrkwtzazdmboabdqii7x5ukm
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods
[article]
2021
arXiv
pre-print
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Finally we discuss limitations, provide guidelines for using interpretability methods and future directions concerning the interpretability of deep neural networks for medical imaging analysis. ...
Therefore, there is a need to ensure interpretability of deep neural networks before they can be incorporated in the routine clinical workflow. ...
[14] used LRP to explain deep neural networks for the diagnosis of Alzheimer's disease. ...
arXiv:2111.02398v1
fatcat:glrfdkbcqrbqto2nrl7dnlg3gq
2020 Index IEEE Journal of Selected Topics in Signal Processing Vol. 14
2020
IEEE Journal on Selected Topics in Signal Processing
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-Concentrate and Explain the Network
Attention. ...
Gamanayake, C., +, JSTSP May 2020 802-816
Computational linguistics
Pragmatic Aspects of Discourse Production for the Automatic Identification
of Alzheimer's Disease. ...
doi:10.1109/jstsp.2020.3029672
fatcat:6twwzcqpwzg4ddcu2et75po77u
Table of Contents
2021
IEEE journal of biomedical and health informatics
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Metric Graph Neural Network Based on a Meta-Learning Strategy for the Diagnosis of Alzheimer's Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Erdogmuş 2928 CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/jbhi.2021.3098092
fatcat:evkfpasb6rhu5hwmnwzlmswts4
Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities
2021
IEEE Access
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[35]
bioRxiv
Deep CNN
NA
To predict the age of the brain to diagnose
Alzheimer's disease. ...
[31]
Frontiers
CNN
Accuracy = 0.34 avg DSC
To segment the ischemic stroke.
Alzheimer's disease
[32]
Springer
Deep CNN
73.75%
Multi-class Alzheimer disease detection and
classification. ...
doi:10.1109/access.2021.3095312
fatcat:3ddvsz5eozav7opv6vvanohcs4
Front Matter: Volume 12032
2022
Medical Imaging 2022: Image Processing
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of SPIE at the time of publication. ...
Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication. ...
Bayesian
for degenerative deformities and osteoporotic fractures with 3D DeepLab 0LGraph interaction for automated diagnosis of thoracic disease using x-ray images0MInteractive deep learning for explainable ...
doi:10.1117/12.2638192
fatcat:ikfgnjefaba2tpiamxoftyi6sa
Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders
2019
European Neurology
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This paper presents a state-of-the-art review of research on automated diagnosis of 5 neurological disorders in the past 2 decades using AI techniques: epilepsy, Parkinson's disease, Alzheimer's disease ...
Authors have been advocating the research ideology that a computer-aided diagnosis (CAD) system trained using lots of patient data and physiological signals and images based on adroit integration of advanced ...
Automated Detection of Alzheimer's Disease Using Brain MRI Images-A Study with Various Feature Extraction Techniques. ...
doi:10.1159/000504292
pmid:31743905
fatcat:frg5lwwt7vauxm6rjgc7sepy6y
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
2021
Sensors
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As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be ...
We also outline the limitations of existing techniques and discuss potential directions for future research. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21144758
fatcat:jytyt4u2pjgvhnhcto3vcvd3a4
Table of Contents
2022
IEEE journal of biomedical and health informatics
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Keegan 103 Joint Optimization of CycleGAN and CNN Classifier for Detection and Localization of Retinal Pathologies on Color Fundus Photographs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Lei 27 Task-Induced Pyramid and Attention GAN for Multimodal Brain Image Imputation and Classification in Alzheimer's Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/jbhi.2021.3134529
fatcat:owhxya3uabgebd2u2jpvmwkcey
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