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disease diagnosis and targeted treatment. One group imagined 3-D goggles that could continuously scan people’s retinas for signs of metastasis in their blood cells, instead of requiring patients to come in every few months for an invasive blood test. As one team member noted, “Who wouldn’t want to watch a 3-D movie with their family and decide if you have disease at the same time?”

Team 8 aimed to develop better architecture to store, curate, and make sense of the data deluge from imaging science. Currently, images collected in biological disciplines, including neuroscience, are stored in different formats, come from a constantly changing array of instruments, and look at different underlying physical phenomena. In addition, databases work well when you know what you are looking for, but they currently lack the tools to explore the data in images in a less directed manner. The team envisioned developing standards for data searches and also imagined an architecture that supports image processing and operates as part of the database. The team developed a concept of exploratory tools that let people collect and analyze image data and imagined using machine learning to anticipate what someone is seeking, even when they’re not quite sure themselves.

At the close of the conference, many researchers noted how valuable it was to speak with people outside their disciplines. Although the current field of imaging science is full of many different languages, for just a few days, researchers spoke a common language. With the avalanche of imaging data expected in the coming years, an ability to tackle broader problems systematically and to find meaning in the madness will only become more important.



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