Advances in biological imaging have given scientists unprecedented datasets with extremely high resolutions, yet data interpretation tools are working overtime to keep up. This is particularly evident in the case of cryo-electron tomograms (cryo-ET), where the samples exhibit inherently low contrast due to the limited electron dose that can be applied during imaging before radiation damage occurs.
The post Berkeley Lab Scientists Create ML Pipeline for Interpreting Large Tomography Datasets appeared first on HPCwire.
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