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Survey: Majority of Imaging Leaders See Important Role for Machine Learning in Radiology


While there is much hype around machine learning and its uses in healthcare, a recent survey indicates that machine learning is not just a buzzword, as 84 percent of medical imaging professionals view the technology as being either important or extremely important in medical imaging. What's more, about 20 percent of medical imaging professionals say they have already adopted machine learning, and about one-third say they will adopt it by 2020.

Have you heard of optoacoustic imaging? This is how it's saving lives


Optoacoustics is similar in some respects to ultrasound imaging. In the latter, a probe sends ultrasonic waves into the body, which the tissue reflects. Sensors in the probe detect the returning sound waves and generate a picture of the inside of the body. Optoacoustic imaging instead sends very short laser pulses into the tissue, where they're absorbed and converted into ultrasonic waves. Similarly to ultrasound imaging, researchers can then detect the waves convert them into images.

NexOptic brings AI solutions to imaging - Electronic Products & Technology


NexOptic Technology Corp., reports that it has made significant advancements to its cutting-edge artificial intelligence (AI) imaging solution. NexOptic's Advanced Low Light Imaging Solution (ALLIS) provides immediate solutions to problems that have plagued the imaging industry for decades. NexOptic's engineered AI drastically reduces image noise common to all imaging systems while improving performance in low light conditions. This is accomplished with NexOptic's expanding suite of patent-pending, deep learning algorithms. Some of the key benefits of ALLIS include: improved low-light performance; dramatic reduction in image noise; improved downstream applications (computational imaging, facial recognition); enhanced long-range image stabilization; major reduction in file sizes.

Sponsored Collection Smarter imaging: Gaining more from your microscopy experiments


This is a fundamental question that drives many biological research programs. Imaging experiments have been trending toward higher-content approaches in order to delve deeper into the mechanism and increase data fidelity. In this digital supplement, we highlight several recent studies from researchers that not only use high-throughput methods but combine them with novel engineering techniques, whether at the specimen or platform level, to gain more from their imaging experiments. This special supplement brought to you by the Science/AAAS Custom Publishing Office.

Interview: "The best image as fast as possible" – Motorola's approach to smartphone imaging


Modern smartphones are essentially pocket-sized mini-computers, capable of dealing with many tasks that not very long ago would have been processed on desktop or laptop computers. The camera module is just one component of many, but more and more consumers are carefully considering camera performance in their buying decision. Manufacturers have been well aware for quite some time and are investing heavily to make sure the cameras on their devices can compete with the best. The device division of mobile communication pioneer Motorola for example, which was taken over by Chinese PC makers Lenovo in 2014, first assembled a dedicated camera and imaging team in 2013 when it was still part of Google. Since then the brand has launched a number of new devices in its Moto range with a clear focus on imaging performance and features.