From artificial intelligence to deep learning in bio-medical applications

dc.creatorMontoya, O.L.Q.
dc.creatorPaniagua, J.G.
dc.date.accessioned2021-04-12T14:09:56Z
dc.date.available2021-04-12T14:09:56Z
dc.date.issued2020-01-01
dc.description.abstractSince 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 2p and a toroidal amplitude band-pass filter, known as the Laguerre-Gauss transform. © Springer Nature Switzerland AG 2020.
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=11959
dc.identifierhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085214210&doi=10.1007%2f978-3-030-42750-4_10&partnerID=40&md5=1bb35329acf816fb283e1d4da6d0bfba
dc.identifier.urihttp://hdl.handle.net/10784/27876
dc.publisherSpringer
dc.relationDOI;10.1007/978-3-030-42750-4_10
dc.relationSCOPUS;2-s2.0-85085214210
dc.rightsSpringer
dc.sourceIntelligent Systems Reference Library
dc.sourceISSN: 18684394
dc.sourceISSN: 18684408
dc.titleFrom artificial intelligence to deep learning in bio-medical applications
dc.typeinfo:eu-repo/semantics/bookPart
dc.typeCapítulo de un Libro
dc.typeinfo:eu-repo/semantics/publishedVersion

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