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Workshop on BDCAT 2020 : Workshop on 7th IEEE/ACM international conference on big data computing, applications and technologies


When Dec 7, 2020 - Dec 10, 2020
Where Leicester, UK
Abstract Registration Due Oct 1, 2020
Submission Deadline Jul 30, 2020
Notification Due Sep 15, 2020
Final Version Due Oct 1, 2020

Call For Papers

Workshop proposal for
7th IEEE/ACM international conference on big data computing, applications and technologies
Big Biomedical Data in Deep Learning Models
Due to the proliferation of biomedical imaging modalities such as Photoacoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, Single Photon Emission Computed Tomography (SPECT), Magnetic Resonance (MR) Imaging, Ultrasound, and Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Electron Tomography and Atomic Force Microscopy, massive amounts of biomedical data are being generated on a daily basis. How can we utilize such big data to build better health profiles and better predictive models so that we can better diagnose and treat diseases and provide a better life for humans? In the past years, many successful learning methods such as deep learning were proposed to answer this crucial question, which has social, economic, as well as legal implications.
Several significant problems plague the processing of big biomedical data, such as data heterogeneity, data incompleteness, data imbalance, and high dimensionality. What is worse is that many data sets exhibit multiple such problems. Most existing learning methods can only deal with homogeneous, complete, class-balanced, and moderate-dimensional data. Therefore, data preprocessing techniques including data representation learning, dimensionality reduction, and missing value imputation should be developed to enhance the applicability of deep learning methods in real-world applications of biomedicine.

This workshop aims to provide a forum for 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.

Link for the main conference:
Link to submission:

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
Biomedical data quality assessment
Transfer learning of biomedical data

Shuihua Wang – University of Leicester –

TPC list:
Yingli Tian, City college of New York,
Shuai Liu, Hunan Normal University,
M. Emre Celebi, University of Central Arkansas,
Vishnu Varthanan, Kalasalingam Academy of Research and Education,
Muhammad Attique khan, COMSATS University Islamabad,
Khan Muhammad, Sejong University,
Yi Chen, Nanjing Normal University,
Fadi Al-Turjman, Near East University,
Zhengchao Dong, Columbia University,
Jiangyi Zhang, Jiangnan University,

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