eScripts is currently undergoing an upgrade and heavy maintenance that may cause instability to the service and unsolicited emails going out to users in the upcoming few days. We apologize for any inconvenience that his may cause you and we appreciate your understanding as we continue to improve your experience.
If you encounter significant issues with eScripts, please let us know at

Multimedia Technology and Enhanced Learning (ICMTEL 2020)

Editorial Board
Lead Guest Editors:
  • Shui-Hua Wang (University of Leicester)
Aims & Scope

Due to the development of numerous biomedical information sensing devices, such as Photoacoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, Single Photon Emission Computed Tomography (SPECT), Magnetic Resonance (MR) Imaging, Ultrasound, Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Electron Tomography and Atomic Force Microscopy, etc. a lot of biomedical information has been gathered these years. In order to process the data, many advanced methods like deep learning were proposed for data analysis, data mining, data tracing and so on because of the excellent performance.

However, a lot of issues appeared in obtaining and processing such big biomedical data, such as data heterogeneity, data missing, data imbalance and high dimensionality of data etc. Moreover, many biomedical datasets simultaneously contain multiple above issues. However, most of the current techniques can only deal with homogeneous, complete, and moderate sized-dimensional data, which
makes the learning of big biomedical data difficult.

Therefore, data processing such as data representation learning, dimensionality reduction, missing value imputation should be developed to bridge the big gap to make the deep learning methods useful for practical applications.


  • Feature extraction by deep learning or sparse codes for biomedical data
  • Data representation of biomedical data
  • Dimensionality reduction techniques (subspace learning, feature selection, sparse screening, feature screening, feature merging, etc) for biomedical data
  • Information retrieval for biomedical data
  • Kernel-based learning for multi-source biomedical data
  • Incremental learning or online learning for biomedical data.
  • Data fusion for multi-source biomedical data
  • Missing data imputation for multi-source biomedical data
  • Data management and mining in biomedical data
  • Web search and meta-search for biomedical data
  • Web information retrieval for biomedical data
  • Biomedical data quality assessment
  • Transfer learning of biomedical data.

Manuscript submission deadline:
Notification of acceptance:
Submission of final revised paper:
Publication of special issue (tentative):

Pervasive Health and Technology
Submission Closed