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Gold digger: Neural networks at the nexus of data science and electron microscopy

 E-Mail IMAGE: (a) A Cropped FRIL image used for input (b) Ground truth image annotated by human experts (c) Network-generated image with annotations d. Discriminator network to discern between real and fake images.. view more  Credit: Max Planck Florida Institute for Neuroscience From sample preparation to image acquisition, electron microscopy (EM) requires precise and time-consuming steps to produce the clarity and detail needed to visualize small cell structures with high resolution. Moreover, once EM images are created, extracting the biological information out of them through analysis can be an even more laborious and time intensive task. Especially because current EM analysis software often requires the skilled eye of a scientist to manually review hundreds of images.

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