International Work-conference on the Interplay between Natural and Artificial Computation

IWINAC2017: Pre-Organized Session:

S06. Signal Processing and Machine Learning applied to Biomedical and NeuroScience Applications

The increasing spread of in vivo imaging technologies, such as Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DTI), functional MRI (fMRI), Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET) and other non-invasive techniques such as Electroencefalography (EEG) or Magnetoencefalography (MEG), have meant a breakthrough in the diagnosis of several pathologies, such as Alzheimer’s Disease, Parkinson Disease, etc. Nowadays, signal processing and machine learning methods are crucial as supporting tools for better undestanding of diseases.

Potential fields of research covered in the session include (but are not limited to):

• Advanced Graph Methods.
• Deep Learning Architectures.
• Emerging Technologies.
• Bayesian networks.
• Static and dynamic graph analysis.
• Prediction networks.
• Sparse modeling.
• Decomposition Methods.

using the aforementioned technologies and other biosignal modalities.

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Last update: October 2012. webmaster@iwinac.org