Special Issue on Biomedical Big Data: Understanding, Learning and Applications

@article{Zhu2017SpecialIO,
  title={Special Issue on Biomedical Big Data: Understanding, Learning and Applications},
  author={Jun Zhu and An-An Liu and Mei Chen and Tolga Tasdizen and Hang Su},
  journal={IEEE Trans. Big Data},
  year={2017},
  volume={3},
  pages={375-377}
}
The papers in this special issue focus on biomedical Big Data. Biomedical imaging is an essential component in various fields of biomedical research and clinical practice. The study of biologists requires continuous monitoring of cell behavior under microscope. Neuroscientists detect regional metabolic brain activity from positron emission tomography (PET), functional magnetic resonance imaging (MRI), and magnetic resonance spectrum imaging (MRSI) scans. During these researching process, large… 
4 Citations
Medical Image Segmentation with Adjustable Computational Complexity Using Data Density Functionals
TLDR
A new method of medical image segmentation with adjustable computational complexity by introducing data density functionals is proposed, under this theoretical framework, several kernels can be assigned to conquer specific predicaments.
Analysis and Visualization Implementation of Medical Big Data Resource Sharing Mechanism Based on Deep Learning
TLDR
The workflow of DBN, a deep learning algorithm, is introduced and the computational characteristics of the algorithm are summarized, and the resource sharing mechanism of the BDMISS system is analyzed.
Ovary Development: Insights From a Three-Dimensional Imaging Revolution
TLDR
This work summarizes the development and function of ovarian compartments that have been delineated by conventional two-dimensional methods and the limits of what can be learned by these approaches and discusses how 3D modeling of the ovary has extended knowledge.