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Multi-Modal Data Analysis for Alzheimer's Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features [article]

Qi Ying, Xin Xing, Gongbo Liang
2021 bioRxiv   pre-print
GWAS and SNPs are frequently used to identify genomic traits. In this study, we propose a multi-modal AD diagnosis neural network that uses both MRIs and SNPs.  ...  Alzheimer's disease (AD) is a devastating neurological disorder primarily affecting the elderly. An estimated 6.2 million Americans age 65 and older are suffering from Alzheimer's dementia today.  ...  CONCLUSION In this study, we propose a novel multi-modal deep neural network. The model uses both MRI and SNPs for Alzheimer's disease diagnosis.  ... 
doi:10.1101/2021.05.07.443184 fatcat:hehj7j3q2vaknemf6jjfsrewuy

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis.  ...  We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  Graphs provide a natural way to represent the population data and model complex interactions by combining features of different modalities for disease analysis [31] .  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis.  ...  We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  [18] proposed an Edge-Variational GCN (EV-GCN) model with a learnable adaptive population graph core to incorporate multi-modal data for uncertaintyaware disease detection.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50

Fulton, Dolezel, Harrop, Yan, Fulton
2019 Brain Sciences  
Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's disease (AD) early facilitates family planning and cost control.  ...  GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.  ...  Acknowledgments: Data were provided [in part] by OASIS: Cross-Sectional: Principal Investigators: D. Marcus, R, Buckner, J, Csernansky J.  ... 
doi:10.3390/brainsci9090212 pmid:31443556 pmcid:PMC6770938 fatcat:t3obp4wx6nepzlt7de3wx7amqa

Diagnosis of STEMI and Non-STEMI Heart Attack using Nature-inspired Swarm Intelligence and Deep Learning Techniques

Mohammad Mahbubur Rahman Khan Mamun, Ali Alouani
2020 Journal of Biomedical Engineering and Biosciences  
On the other hand, there are many 1D biomedical signals, such as ECG, that is more affordable and can be used for medical diagnosis of heart diseases as an example.  ...  has been successfully applied to machine vision, plant disease diagnosis and medical field.  ...  Using the success of 2D CNN for medical imagery data, the combination of optimized feature and 1D CNN can be implemented for time series biomedical data to diagnosis disease.  ... 
doi:10.11159/jbeb.2020.001 fatcat:chfy5mbg35dtnem5xpwwkpvwim

Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities

Dur-E-Maknoon Nisar, Rashid Amin, Noor-Ul-Huda Shah, Mohammed A. Al Ghamdi, Sultan H. Almotiri, Meshrif Alruily
2021 IEEE Access  
SUPERVISED LEARNING MODELS To extract features from the labeled data set, supervised learning models are used for Multi-layer perception.  ...  In the latest heart disease prediction system based on ensemble DL and feature fusion, using sensor data and electronic medical records, EMR combines features to generate records.  ... 
doi:10.1109/access.2021.3095312 fatcat:3ddvsz5eozav7opv6vvanohcs4

Multimodal Classification: Current Landscape, Taxonomy and Future Directions [article]

William C. Sleeman IV, Rishabh Kapoor, Preetam Ghosh
2021 arXiv   pre-print
Multimodal classification research has been gaining popularity in many domains that collect more data from multiple sources including satellite imagery, biometrics, and medicine.  ...  Many of the most difficult aspects of unimodal classification have not yet been fully addressed for multimodal datasets including big data, class imbalance, and instance level difficulty.  ...  Most works also used the same algorithm for learning and classification unless it was a multi-task problem or used an ensemble.  ... 
arXiv:2109.09020v1 fatcat:yagsbnxeefcpneqwgflrxxioqa

2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24

2020 IEEE journal of biomedical and health informatics  
., and Inan, O.T., 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  ...  2020 3529-3538 Honda, O., see Xu, R., 2041-2052 Hong, H., see 2833-2843 Hong, H., see Xue, B., JBHI Feb. 2020 614-625 Hoog Antink, C., Mai, Y., Aalto, R., Bruser, C., Leonhardt, S., Oksala, N., and  ...  ., +, JBHI Jan. 2020 160-170 Characterizing Alzheimer's Disease With Image and Genetic Biomarkers Using Supervised Topic Models.  ... 
doi:10.1109/jbhi.2020.3048808 fatcat:iifrkwtzazdmboabdqii7x5ukm

Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks [article]

Zhifei Zhang, Wanling Gao, Fan Zhang, Yunyou Huang, Shaopeng Dai, Fanda Fan, Jianfeng Zhan, Mengjia Du, Silin Yin, Longxin Xiong, Juan Du, Yumei Cheng, Xiexuan Zhou, Rui Ren (+2 others)
2019 arXiv   pre-print
Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data?  ...  Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks?  ...  Shi et al. [141] develop a multi-modal stacked deep polynomial networks (MM-SDPN) algorithm to fuse and learn feature representation from multi-modal neuroimaging data for AD diagnosis, and the approaches  ... 
arXiv:1901.00642v1 fatcat:fak46q7bgzesll6y4h7i6mcysi

Multimodal Classification: Current Landscape, Taxonomy and Future Directions

William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh
2022 ACM Computing Surveys  
Many of the most difficult aspects of unimodal classification have not yet been fully addressed for multimodal datasets including big data, class imbalance, and instance level difficulty.  ...  Multimodal classification research has been gaining popularity with new datasets in domains such as satellite imagery, biometrics, and medicine.  ...  Most works also used the same algorithm for learning and classiication unless it was a multi-task problem or used an ensemble as Primary Learner -Final Classiier stages were shared in 11 of the 18 models  ... 
doi:10.1145/3543848 fatcat:ejigpgm5gnabvc4jrb3nml5l4y

2021 Index IEEE Journal of Biomedical and Health Informatics Vol. 25

2021 IEEE journal of biomedical and health informatics  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  ., +, JBHI Feb. 2021 485-492 An L 0 Regularization Method for Imaging Genetics and Whole Genome Association Analysis on Alzheimer's Disease.  ... 
doi:10.1109/jbhi.2022.3140980 fatcat:ufig7b54gfftnj3mocspoqbzq4

Medical image fusion: A survey of the state of the art

Alex Pappachen James, Belur V. Dasarathy
2014 Information Fusion  
of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.  ...  to increase the clinical applicability of medical images for diagnosis and assessment of medical problems.  ...  of perceptual system of brain [ 302] , ensemble based data fusion for diagnosis of Alzheimer's disease [303] , filter bank selection for brain computer interaction [304] , feature based fusion of brain  ... 
doi:10.1016/j.inffus.2013.12.002 fatcat:balzov6qsbdxnkfcwcltpx7uba

Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks

Zhifei Zhang, Wanling Gao, Fan Zhang, Yunyou Huang, Shaopeng Dai, Fanda Fan, Jianfeng Zhan, Mengjia Du, Silin Yin, Longxin Xiong, Juan Du, Yumei Cheng (+4 others)
2019 IEEE Access  
Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data?  ...  Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks?  ...  [141] develop a multi-modal stacked deep polynomial networks (MM-SDPN) algorithm to fuse and learn feature representation from multi-modal neuroimaging data for AD diagnosis, and the approaches reach  ... 
doi:10.1109/access.2019.2891948 fatcat:d7yabam6zbdpxdojd4vfeth2rq

Table of Contents

2018 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)  
, tools, security attacks and defense mechanisms in context of Internet of Things Diagnosis of Alzheimer's Disease using Machine Learning 135 Machine learning approach to forecast early stage recognition  ...  actionable Genetic Mutations based on clinical evidences 112 Prediction of Diseases and Suggestion of Appropriate Medicines 113 Computational Intelligence Model for Code Generation from Natural  ... 
doi:10.1109/iccubea.2018.8697655 fatcat:jvjgmcrh3fhxtkf4kyydawnkiq

Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications [article]

Kexin Ding, Mu Zhou, Zichen Wang, Qiao Liu, Corey W. Arnold, Shaoting Zhang, Dimitri N. Metaxas
2022 arXiv   pre-print
Yet daunting challenges remain for designing the important image-to-graph transformation for multi-modality medical imaging and gaining insights into model interpretation and enhanced clinical decision  ...  We discuss the fast-growing use of graph network architectures in medical image analysis to improve disease diagnosis and patient outcomes in clinical practice.  ...  Multi-modality MRI data analysis is able to deepen our understanding of disease diagnosis from different data aspects.  ... 
arXiv:2202.08916v3 fatcat:zskcqvgjpnb6vdklmyy5rozswq
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