International Workshop on Data Preprocessing for Big Biomedical Data in Deep Learning Models (DPBBD 2020)
Chairs of the Workshop: Dr. Sharon Shui-Hua Wang, University of Leicester
Dr. Zhengchao Dong, Columbia University Medical Center: Division of Translational Imaging
Due to numerous biomedical information sensing devices, such as, Computed Tomography (CT), Magnetic Resonance (MR) Imaging, Ultrasound, Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET), to Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy, etc. Large amount of biomedical information was gathered these years. However, a lot of issues appear 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 data sets simultaneously contain multiple 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 preprocessing including data representation learning, dimensionality reduction, missing value imputation should be developed to solve the big gap to make the machine learning methods used for the practical applications.
The purpose of this workshop aims to provide a diverse, but complementary set of contributions to discuss and demonstrate new developments and applications that cover existing above issues in data preprocessing of 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.
The accepted papers will be published in the proceedings of ICMTEL 2020 – 2nd EAI International Conference on Multimedia Technology and Enhanced Learning, Springer’s Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
Areas of interest include, but are not limited to:
|– 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|
As in previous years, the conference intends to attract research community from around the world. Each submission will be reviewed by at least three independent experts.
Technical Program Committee of the Workshop
Zhenchao Dong, Columbia University, United States
Muhammad Sajjad, Islamia College Peshawar, Pakistan
Zheng Zhang, The University of Queensland, Australia
Khan Muhammad, Sejong University, South Korea
Irfan Mehmood, Sejong University, South Korea
Rafik Hamza, Batna 2 University, Algeria
Ran Li, Xinyang Normal University, Xinyang, China
Po Yang, Liverpool John Moores University, United Kingdom
Zehong (Jimmy) Cao, University of Technology Sydney, Australia
Victor Hugo C. de Albuquerque, Universidade de Fortaleza, Brazil
Mohamed Elhoseny, University of North Texas, United States
Sambit Bakshi, National Institute of Technology Rourkela, India
Yi Chen, Nanjing Normal University, China
Yuankai Huo, Vanderbilt University, United States
Sidan Du, Nanjing University, China
Submission deadline: 15 October 2019
Authors’ notification: 9 December 2019
Camera-ready deadline: 20 January 2020
Conference dates: 10 April 2020
All registered papers will be published together with ICMTEL Proceedings by Springer and made available through SpringerLink Digital Library.
ICMTEL proceedings are subjected for indexing in leading indexing services, including Ei Compendex, ISI Web of Science, Scopus, CrossRef, Google Scholar, DBLP, as well as EAI’s own EU Digital Library (EUDL).
Additional publication opportunities in journals are being discussed.
Papers should be submitted through EAI ‘ConfyPlus‘ system, and have to comply with the Springer format (see Author’s kit section). Papers should be in English. Regular papers should be no less than 4 pages and no more than 10 pages LNCS format.
Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. Papers will be selected based on their originality, timeliness, significance, relevance, and clarity of presentation.
Paper review will be single-blind. Previously published work may not be submitted, nor may the work be concurrently submitted to any other conference or journal. Such papers will be rejected without review. The paper must start with a title, an abstract, and keywords.
Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work.