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CMIM-DPBD 2020 : Data Preprocessing for Big Biomedical Data in Deep Learning Models


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Submission Deadline TBD

Call For Papers

Data Preprocessing for Big Biomedical Data in Deep Learning Models

Due to 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. Large amounts of biomedical information were gathered these years. Many advanced methods like deep learning are proposed for data analysis, data mining, data tracing because of the excellent performance.

However, a lot of issues appear in obtaining and processing such big biomedical data. For example, the data are generally incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data. There are many reasons for missing data such as data is not continuously collected, a mistake in data entry, technical problems with bio-metrics and much more. Noisy:  The reasons for the existence of noisy data could be a technological problem of gadget that gathers data, a human mistake during data entry and much more. Inconsistent: The presence of inconsistencies is due to the reasons such that existence of duplication within data, human data entry, containing mistakes in codes or names, i.e., violation of data constraints and much more.

Therefore, data processing including data representation learning, data transforming, dimensionality reduction, missing value imputation to handle the raw data should be developed to solve the big gap to make the deep learning methods used for the practical applications.

This special issue aims to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications that cover existing above issues in data processing of big biomedical data. We would also like to accept successful applications of the new methods, including but not limited to data processing, analysis, and knowledge discovery of big biomedical data.

Dear Sir/Madam,

I am pleased to invite you to contribute to my thematic issue entitled "Data Preprocessing for Big Biomedical Data in Deep Learning Models" in the "Current Medical Imaging". Please find below the submission link for the thematic issue.


Sharon Shui-Hua Wang

Current Medical Imaging

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