Collection of large amounts of data often results in high-quality, highly informative data intermixed with data that is either of poor quality or of little interest in relation to the question at hand. Wieslander’s thesis work will focus on development of methods for identify non-informative data early on in the analysis process; either online at data collection, or off-line prior to full data analysis. Examples of work involves hierarchical analysis of whole slide tissue images where informative regions are detected in low resolution and further processed in higher resolution, deep learning based image restoration for transmission electron microscopy for improved and optimized imaging and analysis and cloud based streaming for image data optimized for large object sizes. Read more on http://haste.research.it.uu.se/
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