Description
Afternoon session of the first day
The significant increase in volume and complexity of data resulting from technological development is a common challenge faced by many scientific disciplines. More sensitive detectors and large scale experiments are currently overwhelming researchers who turn to automatic machine learning algorithms in order to filter, order, or pre-select potentially interesting subsets of the original data...
In the last decade, convolutional neural networks (CNNs) have revolutionized the field of image processing and have become increasingly popular among astronomers for morphological analysis of galaxies. This push has been driven by the fact that they are the perfect alternative to the traditional techniques of obtaining morphological classifications --- expert visual classification, citizen...
A major trend in astronomy and healthcare is the rapid adoption of Bayesian statistics for data analysis and modeling. With modern data-sets growing by orders of magnitude in size, the focus is now on developing methods capable of applying contemporary inference techniques to extremely large datasets. To this aim, I present PyAutoFit (https://github.com/rhayes777/PyAutoFit), an open-source...
Interpolation is critical to filling in gaps in data regardless of the field. Objects closeness in distance can easily be leveraged to greatly improve interpolation. In this talk I will briefly introduce the Gaussian process and show why it is a popular tool for spatial analysis. I will then give a brief tour of computational and theoretical aspects in the current state-of-the-art.
Multispectral, multiplex immunofluorescence (mIF) enables the study of the complex tumor microenvironment (TME) through quantification of key immunomodulatory marker co-expression patterns on specific cells and spatial relationships between different cell types. We have developed a detailed, automated, multi-step approach to mIF staining and image analysis that can support the standardization...
Historically, analysis of microscope images has relied mainly upon extensive manual review by experienced medical professionals, but recent developments in microscope technology, biological workflows, and data handling capabilities have opened the door to new ways of thinking about microscopy data. The AstroPath group is working to translate a host of "big data" methods familiar in astronomy...
