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posted by mrpg on Saturday March 24 2018, @05:43PM   Printer-friendly
from the 2400-dpi dept.

Submitted via IRC for SoyCow3941

"An essential part of the clinical imaging pipeline is image reconstruction, which transforms the raw data coming off the scanner into images for radiologists to evaluate," says Bo Zhu, PhD, a research fellow in the MGH Martinos Center and first author of the Nature paper. "The conventional approach to image reconstruction uses a chain of handcrafted signal processing modules that require expert manual parameter tuning and often are unable to handle imperfections of the raw data, such as noise. We introduce a new paradigm in which the correct image reconstruction algorithm is automatically determined by deep learning artificial intelligence.

"With AUTOMAP, we've taught imaging systems to 'see' the way humans learn to see after birth, not through directly programming the brain but by promoting neural connections to adapt organically through repeated training on real-world examples," Zhu explains. "This approach allows our imaging systems to automatically find the best computational strategies to produce clear, accurate images in a wide variety of imaging scenarios."

Source: New artificial intelligence technique dramatically improves the quality of medical imaging


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  • (Score: 2, Insightful) by Anonymous Coward on Saturday March 24 2018, @06:11PM (1 child)

    by Anonymous Coward on Saturday March 24 2018, @06:11PM (#657608)

    Is it also going to misidentify things organically? Or miss details organically?

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  • (Score: 2) by RS3 on Sunday March 25 2018, @12:49AM

    by RS3 (6367) on Sunday March 25 2018, @12:49AM (#657722)

    In music creation "sequencer" software they often have a "humanize" function that pseudo-randomizes the beats. It should be easy to blend in some random results to "warm it up" until the economy can adapt.