Journal Information
IEEE Transactions on Big Data (TBD)

Call For Papers
The IEEE Transactions on Big Data publishes peer reviewed articles with big data as the main focus. The articles will provide cross disciplinary innovative research ideas and applications results for big data including novel theory, algorithms and applications. Research areas for big data include, but are not restricted to, big data analytics, big data visualization, big data curation and management, big data semantics, big data infrastructure, big data standards, big data performance analyses, intelligence from big data, scientific discovery from big data security, privacy, and legal issues specific to big data. Applications of big data in the fields of endeavor where massive data is generated are of particular interest.
Last updated by Xin Yao in 2016-07-03
Special Issues
Special Issue on Biomedical Big Data: Understanding, Learning and Applications
Submission Date: 2017-03-01

Biomedical imaging is an essential component in various fields of biomedical research and clinical practice. Biologists quantitatively study cell behavior and generate high-throughput microscopy data sets. Neuroscientists detect regional metabolic brain activity from positron emission tomography (PET), functional magnetic resonance imaging (MRI), and magnetic resonance spectrum imaging (MRSI) scans. Virologists generate 3D reconstructions of viruses from micrographs, and radiologists identify and quantify tumors from MRI and computed tomography (CT) scans. Advanced imaging equipment and diverse applications have driven the generation of biomedical big data. The main challenge and bottleneck for the related research is the conversion of "biomedical big data" int- interpretable information and hence discoveries. Computer vision theory has a huge potential in many aspects for automated understanding of biomedical data and has been used successfully t- speed up and improve applications such as large-scale cell image analysis (image preconditioning, cell segmentation and detection, cell tracking, and cell behavior identification), image reconstruction and registration, organ segmentation and disease classification. Moreover, when it comes t- the new era of machine learning, deep learning has revolutionized multiple fields of computer vision, significantly pushing the state of arts of computer vision systems in a broad array of high-level tasks. This special issue serves as a forum t- bring together active researchers all over the world t- share their recent advances in this exciting area. We solicit original contributions in three-fold: (1) present state-of-the-art theories and novel application scenarios related t- biomedical big data analytics; (2) survey the recent progress in this area; and (3) build benchmark datasets. The topics of interest for this special issue include, but are not limited to: 1.Biomedical Big Data Representation - Robust feature extraction - Data-driven feature learning - Large-scale biomedical data acquisition - Novel datasets and benchmark for specific biological applications (e.g. cell image analysis, image segmentation, shape analysis) 2.Biomedical Big Data Understanding - Image restoration - Image segmentation - Image Registration - Object detection & tracking - Event Detection - Biomedical big data organization, retrieval and indexing - Health, economics and other applications over biomedical big data 3.Biomedical Big Data Learning - Time-series modeling - Transfer learning - Multi-task learning - Sparse Coding - Weakly supervised learning
Last updated by Dou Sun in 2016-09-25
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