posted by organizer: liuanan || 4213 views || tracked by 15 users: [display]

TBD 2017 : IEEE Transactions on Big Data Special Issue on Biomedical Big Data: Understanding, Learning and Applications


When N/A
Where N/A
Submission Deadline Mar 1, 2017
Notification Due Sep 1, 2017
Final Version Due Oct 1, 2017
Categories    bio-medical image processing   computer vision   machine learning   big data

Call For Papers

Biomedical imaging is an essential component in various fields of biomedical research
and clinical practice. Biologists quantitatively study cell behavior and generate highthroughput 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” into 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 to 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 to
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 to bring together active researchers all over the
world to 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 to 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:
 Biomedical Big Data Representation
o Robust feature extraction
o Data-driven feature learning
o Large-scale biomedical data acquisition
o Novel datasets and benchmark for specific biological applications (e.g. cell
image analysis, image segmentation, shape analysis)
 Biomedical Big Data Understanding
o Image restoration
o Image segmentation
o Image Registration
o Object detection & tracking
o Event Detection
o Biomedical big data organization, retrieval and indexing
o Health, economics and other applications over biomedical big data
 Biomedical Big Data Learning
o Time-series modeling
o Transfer learning
o Multi-task learning
o Sparse Coding
o Weakly supervised learning

Related Resources

ICML 2017   34th International Conference on Machine Learning
NIPS 2017   The Thirty-first Annual Conference on Neural Information Processing Systems
ADAH 2017   Advanced Data Analytics in Health
ECML-PKDD 2017   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
IEEE Big Data 2017   2017 IEEE International Conference on Big Data
DSAA 2017   The 4th IEEE International Conference on Data Science and Advanced Analytics 2017
IEEE Bigcomp 2018   2018 IEEE International Conference on Big Data and Smart Computing
BMVC 2017   British Machine Vision Conference
COMPLEX NETWORKS 2017   6th International Conference on Complex Networks and Their Applications
SoICT 2017   The Eighth Interntional Symposium on Information and Communication Technology