Learning and Understanding of Biomedical Big Data Call for Papers

Special Issue on Learning and Understanding of Biomedical Big Data,
Machine Vision Applications, Springer. The details are appended below
(also at

Summary and Scope:
High-throughput imaging technologies have enabled researchers and
practitioners to acquire large volumes of biomedical images
automatically everyday. This has made it possible to conduct
large-scale, image-based experiments for biomedical discovery. The
main challenge and bottleneck in such experiments is the conversion of
"biomedical big data" into interpretable information and hence
discoveries. Computer vision has huge potential for automated analysis
and understanding of such data, including image segmentation, object
detection, shape analysis, object tracking, event detection, and
computer-aided diagnosis. Not only do computers have more
"stamina" than human annotators for such tasks, they also
perform analysis that is more reproducible and less subjective. Recent
years, novel machine learning techniques, especially deep learning,
have revolutionized multiple areas in computer vision and
significantly advanced the state-of-art.

This special issue serves to attract active researchers around the
world to share their recent innovation in this exciting area. We
solicit original contributions in three-fold: (1) present
state-of-the-art theories and novel applications in biomedical big
data analysis; (2) survey the recent progress in this area; and (3)
build benchmark datasets.

Submission Deadline: July 14, 2017