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