This workshop focuses on the challenges presented by the analysis and visualization of large data sets that are collected in biomedical imaging, genomics and proteomics. The sheer size of data (easily in the range of terabytes, and growing) requires computationally efficient techniques for the sampling, representation, organization, and filtering of data; ideas and techniques from signal processing, geometric and topological analysis, stochastic dynamical systems, machine learning and statistical modeling are needed to extract patterns and characterize features of interest. Visualization enables interaction with data, algorithms, and outputs.
Data sets from biomedical imaging, genomics and proteomics often have unique characteristics that differentiate them from other data sets, such as extremely high-dimensionality, high heterogeneity due to different data modalities (across different spatial and temporal scales, but also across different biological layers) that need to be fused, large stochastic components and noise, low sample size and possibly low reproducibility of per-patient data. These unique aspects, as well as the large size, pose challenges to many existing techniques aimed at solving the problems above.
The workshop will bring together biologists, computer scientists, engineers, mathematicians and statisticians working in a wide of areas of expertise, with the goal of pushing existing techniques, and developing novel ones, for tackling the unique challenges offered by large data sets in biomedical imaging.