extended-abstract

Deep Learning in Medical Imaging: fMRI Big Data Analysis via Convolutional Neural Networks

Authors:

Amirhessam Tahmassebi,

Amir H. Gandomi,

Mieke H. J. Schulte,

Anna E. Goudriaan,

Anke Meyer-BaeseAuthors Info & Claims

PEARC '18: Proceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity

Article No.: 85, Pages 1 - 4

https://doi.org/10.1145/3219104.3229250

Published: 22 July 2018 Publication History

Abstract

This paper aims at implementing novel biomarkers extracted from functional magnetic resonance imaging (fMRI) images taken at resting-state using convolutional neural networks (CNN) to predict relapse in heavy smoker subjects. In this regard, two classes of subjects were studied. The first class contains 19 subjects that took the drug N-acetylcysteine (NAC), and the second class contains 20 subjects that took a placebo. The subjects underwent a double-blind smoking cessation treatment. The resting-state fMRI of the subjects' brains were recorded through 200 snapshots before and after the treatment. The relapse data was assessed after 6 months past the treatment. The data was pre-processed and an undercomplete autoencoder along with various similarity metrics was developed to extract salient features that could differentiate the pre and post treatment images. Finally, the extracted feature matrix was fed into robust classification algorithms to classify the subjects in terms of relapse and non-relapse. The XGBoost algorithm with 0.86 precision and an AUC of 0.92 outperformed the other classification methods in prediction of relapse in subjects.

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Amirhessam Tahmassebi, Ali Moradi Amani, Katja Pinker-Domenig, and Anke Meyer-Baese. 2018. Determining disease evolution driver nodes in dementia networks. In Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, Vol. 10578. International Society for Optics and Photonics, 1057829.

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Amirhessam Tahmassebi, Amir H Gandomi, Ian McCann, Mieke HJ Schulte, Lianne Schmaal, Anna E Goudriaan, and Anke Meyer-Baese. 2017. An evolutionary approach for fMRI big data classification. In Evolutionary Computation (CEC), 2017 IEEE Congress on. IEEE, 1029--1036.

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Amirhessam Tahmassebi, Amir H Gandomi, Ian McCann, Mieke HJ Schulte, Lianne Schmaal, Anna E Goudriaan, and Anke Meyer-Baese. 2017. fMRI Smoking Cessation Classification Using Genetic Programming. In Workshop on Data Science meets Optimization. http://ds-o.org/images/Workshop_papers/Gandomi.pdf

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Amirhessam Tahmassebi, Amir H. Gandomi, and Anke Meyer-Bäse. 2017. High Performance GP-Based Approach for fMRI Big Data Classification. In Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact (PEARC17). ACM, New York, NY, USA, Article 57, 4 pages.

Digital Library

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Amirhessam Tahmassebi, Amir H Gandomi, Mieke HJ Schulte, Anna E Goudriaan, Simon Y Foo, and Anke Meyer-Baese. 2018. Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification. Complexity 2018 (2018).

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Amirhessam Tahmassebi, Katja Pinker-Domenig, Georg Wengert, Marc Lobbes, Andreas Stadlbauer, Francisco J Romero, Diego P Morales, Encarnacion Castillo, Antonio Garcia, Guillermo Botella, et al. 2017. Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia. In Smart Biomedical and Physiological Sensor Technology XIV, Vol. 10216. International Society for Optics and Photonics, 1021609.

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Liang YLong MYang PWang TJiao JLei B(2023)Fused Brain Functional Connectivity Network and Edge-attention Graph Convolution Network for Fibromyalgia Syndrome Diagnosis2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC40787.2023.10340485(1-5)Online publication date: 24-Jul-2023
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Ahmed SAlam MHassan MRozbu MIshtiak TRafa NMofijur MShawkat Ali AGandomi A(2023)Deep learning modelling techniques: current progress, applications, advantages, and challengesArtificial Intelligence Review10.1007/s10462-023-10466-856:11(13521-13617)Online publication date: 17-Apr-2023
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Index Terms

Deep Learning in Medical Imaging: fMRI Big Data Analysis via Convolutional Neural Networks
1. Computing methodologies
  1. Machine learning

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cover image ACM Other conferences

PEARC '18: Proceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity

July 2018

652 pages

ISBN:9781450364461

DOI:10.1145/3219104

General Chair:
Sergiu Sanielevici
Pittsburgh Supercomputing Center

Copyright © 2018 Owner/Author.

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: 22 July 2018

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Extended-abstract
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PEARC '18

PEARC '18: Practice and Experience in Advanced Research Computing

July 22 - 26, 2018

PA, Pittsburgh, USA

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PEARC '18 Paper Acceptance Rate 79 of 123 submissions, 64%;

Overall Acceptance Rate 133 of 202 submissions, 66%

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Cited By

Li JWang SRudinac SOsseyran A(2024)High-performance computing in healthcare: An automatic literature analysis perspectiveJournal of Big Data10.1186/s40537-024-00929-211:1Online publication date: 2-May-2024
https://doi.org/10.1186/s40537-024-00929-2
Liang YLong MYang PWang TJiao JLei B(2023)Fused Brain Functional Connectivity Network and Edge-attention Graph Convolution Network for Fibromyalgia Syndrome Diagnosis2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC40787.2023.10340485(1-5)Online publication date: 24-Jul-2023
https://doi.org/10.1109/EMBC40787.2023.10340485
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https://dl.acm.org/doi/10.1007/s10462-023-10466-8
Krajevski JIvanoska ITrivodaliev KKalajdziski SGievska S(2023)An Exploration of Autism Spectrum Disorder Classification from Structural and Functional MRI ImagesICT Innovations 2022. Reshaping the Future Towards a New Normal10.1007/978-3-031-22792-9_14(175-189)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-3-031-22792-9_14
Aggarwal KManso Jimeno MRavi KGonzalez GGeethanath S(2023)Developing and deploying deep learning models in brain magnetic resonance imaging: A reviewNMR in Biomedicine10.1002/nbm.501436:12Online publication date: 4-Aug-2023
https://doi.org/10.1002/nbm.5014
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Yin WLi LWu F(2022)Deep learning for brain disorder diagnosis based on fMRI imagesNeurocomputing10.1016/j.neucom.2020.05.113469:C(332-345)Online publication date: 16-Jan-2022
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Unyi DInsalata FVeličković PGyires-Tóth B(2022)Utility of Equivariant Message Passing in Cortical Mesh SegmentationMedical Image Understanding and Analysis10.1007/978-3-031-12053-4_31(412-424)Online publication date: 27-Jul-2022
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