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Medical Image Classification

Special Issue Information

Dear Colleagues,

Given the plethora of new instruments and sensors (some now attached to cell phones), medical data are accumulating at an unprecedented rate, and there is no reason to believe that the complexity and the amount of data will do anything other than continue to snowball. More than ever, machine learning algorithms are needed to realize the potential for medical science that is embedded in this avalanche of data.

The purpose of this TC is to collect studies representing the state-of-the-art in medical image classification from many different modalities: computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), functional MRI (fMRI), electroencephalography (EEG), etc. Multimodal systems are especially, though not exclusively, solicited.

Prof. Dr. Sheryl Berlin Brahnam
Prof. Dr. Loris Nanni
Dr. Rick Brattin
Collection Editors

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Keywords

  • biomedical image analysis
  • generative adversarial networks
  • multimodal images
  • deep learners
  • convolutional neural networks
  • image segmentation
  • feature learning
  • image augmentation
  • ensembles
  • descriptors

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