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From Artificial Intelligence to Deep Learning in Bio-medical Applications

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Deep Learners and Deep Learner Descriptors for Medical Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 186))

Abstract

Since their introduction in late 80s, convolutional neural networks and auto-encoder architectures have shown to be powerful for automatic feature extraction and information simplification. Using convolution kernels from image processing in 2D and 3D spaces for the stage by stage features retrieval processes, allows the architecture to be as flexible as the designer wants, considering that this is not a lucky fact. With the recent ten years of technological progress now we can compute and train those architectures and they have faced so many challenges for applications originating the most famous CNN architectures. This chapter presents an author position related to the artificial intelligence field and machine learning/deep learning appearance in the scientific world scene describing hastily the basis for each one and later, focusing on medical applications most of the socialized on the Annual IEEE Engineering in Medicine and Biology Society conference held in Hawaii in July 2018. While addressing the medical applications from cardiovascular to cancer diagnosis, we will briefly describe the architectures and discuss some features. Finally, we will present a contribution to the deep learning by introducing a new architecture called Convolutional Laguerre-Gauss Network with a kernel based on a spiral phase function ranging from 0 to 2π and a toroidal amplitude band-pass filter, known as the Laguerre-Gauss transform.

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Notes

  1. 1.

    Typical in transfer learning can be seen in http://www.image-net.org/.

  2. 2.

    However, CNNs with large receptive fields, such as very deep networks or networks with large convolution kernels, often suffer from overfitting due to large numbers of trainable parameters.

  3. 3.

    https://lmb.informatik.uni-freiburg.de/index.php.

  4. 4.

    From the Department of Nuclear Medicine, Seoul National University College of Medicine.

  5. 5.

    With 1000, and 200 neurons with hyper-tangent activation functions for two hidden layers, and a single output neuron with a sigmoid activation function.

  6. 6.

    Speech becomes hesitant and lacks grammatical accuracy.

  7. 7.

    Lose ability to understand or formulate words in a spoken sentence.

  8. 8.

    There is a direct relation on the physics for RTM and medical imaging check the work of Wang et al. 2016 in IEEE Transactions on Medical Imaging, vol. 35.

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Authors and Affiliations

  1. Mathematical Sciences Department at Universidad EAFIT, Medellin, Colombia

    Olga Lucia Quintero Montoya

  2. Engineering Faculty, Instituto Tecnológico Metropolitano, Medellin, Colombia

    Juan Guillermo Paniagua

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  1. Olga Lucia Quintero Montoya
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  2. Juan Guillermo Paniagua
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Correspondence to Olga Lucia Quintero Montoya .

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Editors and Affiliations

  1. Department of Information Engineering, University of Padova, Padova, Italy

    Loris Nanni

  2. Computer Information Systems, Missouri State University, Springfield, MO, USA

    Sheryl Brahnam

  3. Department of Information Technology and Cybersecurity, Missouri State University, Springfield, MO, USA

    Rick Brattin

  4. Department of Information Engineering, Intelligent Autonomous Systems Lab, Padova, Padova, Italy

    Stefano Ghidoni

  5. University of Technology, Sydney, Australia

    Lakhmi C. Jain

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Montoya, O.L.Q., Paniagua, J.G. (2020). From Artificial Intelligence to Deep Learning in Bio-medical Applications. In: Nanni, L., Brahnam, S., Brattin, R., Ghidoni, S., Jain, L. (eds) Deep Learners and Deep Learner Descriptors for Medical Applications. Intelligent Systems Reference Library, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-030-42750-4_10

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