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Quantifying Inter-hemispheric Differences in Parkinson’s Disease Using Siamese Networks

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Book cover Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (IWINAC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13258))

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Abstract

Classification of medical imaging is one of the most popular application of intelligent systems. A crucial step is to find the features that are relevant for the subsequent classification. One possibility is to compute features derived from the morphology of the target region in order to check its role in the pathology under study. It is also possible to extract relevant features to evaluate the similarity between different regions, in addition to compute morphology-related measures. However, it can be much more useful to model the differences between regions. In this paper, we propose a method based on the principles of siamese neural networks to extract informative features from differences between two brain regions. The output of this network generates a latent space that characterizes differences between the two hemispheres. This output vector is then fed into a linear SVM classifier. The usefulness of this method has been assessed with images from the Parkinson’s Progression Markers Initiative, demonstrating that differences between the dopaminergic regions of both hemispheres lead to a high performance when classifying controls vs Parkinson’s disease patients.

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Acknowledgments

This work was supported by projects PGC2018-098813-B-C32 and RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), UMA20-FEDERJA-086, CV20-45250, A-TIC-080-UGR18 and P20 00525 (Consejería de economía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF); and by Spanish “Ministerio de Universidades” through Margarita-Salas grant to J.E. Arco.

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Authors and Affiliations

  1. Communications Engineering Department, University of Málaga, 29004, Málaga, Spain

    Juan E. Arco & A. Ortiz

  2. Department of Signal Theory, Communications and Networking, University of Granada, 18060, Granada, Spain

    Diego Castillo-Barnes, Juan M. Górriz & Javier Ramírez

  3. Andalusian Data Science and Computational Intelligence Institute (DaSCI), Granada, Spain

    Juan E. Arco, A. Ortiz, Diego Castillo-Barnes, Juan M. Górriz & Javier Ramírez

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  1. Juan E. Arco
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  2. A. Ortiz
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  3. Diego Castillo-Barnes
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  4. Juan M. Górriz
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  5. Javier Ramírez
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Corresponding author

Correspondence to Juan E. Arco .

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Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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Arco, J.E., Ortiz, A., Castillo-Barnes, D., Górriz, J.M., Ramírez, J. (2022). Quantifying Inter-hemispheric Differences in Parkinson’s Disease Using Siamese Networks. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_16

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06241-4

  • Online ISBN: 978-3-031-06242-1

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