![]() ![]() ![]() Vice versa, life scientists increasingly rely on algorithms or crowdsourced outputs in combination with proofreading tools to enable efficient analysis of very large image sets.īioimage informatics aims at developing software to ease the analysis of large-scale biomaging data ( Myers, 2012). computer scientists require realistic ground truth and proofreadings ( Ground-truth data cannot do it alone, 2011) provided by life scientists to design and refine their analysis methods. Furthermore, all these individuals need to actively collaborate to gain new insights, e.g. ![]() Developers of image processing algorithms are willing to collaborate with machine learning specialists to build complementary image analysis workflows. For example, researchers in experimental histology are willing to precisely annotate images and need to consult distant experts in pathology or molecular biology. EyeWire ( ) and Brainflight ( ) projects). In these fields, significant advances could be made by multidisciplinary collaboration involving distributed groups of life scientists and computer scientists exploiting large-scale image networks ( Moody et al., 2013 Poldrack, 2014), or eventually by enlisting the help of members of the general public in large imaging surveys ( Clery, 2011) through interactive games (e.g. biomedical research studies often rely on whole-slide virtual microscopy or automated volume electron microscopy. biology, biomedicine, astronomy, botany, geology, paleobiology, marine research, aerobiology, climatology), projects leading to terabytes of multi-gigapixel images become increasingly common ( The data deluge, 2012) e.g. ![]()
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