<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>AIMOS - pre-trained models</dcterms:title><dcterms:identifier>https://doi.org/10.7910/DVN/G6VLZN</dcterms:identifier><dcterms:creator>Schoppe, Oliver</dcterms:creator><dcterms:publisher>Harvard Dataverse</dcterms:publisher><dcterms:issued>2020-04-28</dcterms:issued><dcterms:modified>2020-04-28T04:37:16Z</dcterms:modified><dcterms:description>Pre-trained models for AIMOS &#xd;
* Unet768 for native micro-CT&#xd;
* Unet768 for contrast-enhanced micro-CT&#xd;
* Unet768 for light-sheet microscopy</dcterms:description><dcterms:subject>Computer and Information Science</dcterms:subject><dcterms:subject>Medicine, Health and Life Sciences</dcterms:subject><dcterms:date>2020-04-28</dcterms:date><dcterms:contributor>Schoppe, Oliver</dcterms:contributor><dcterms:dateSubmitted>2020-04-27</dcterms:dateSubmitted><dcterms:relation>https://www.nature.com/articles/sdata2018294,&#xd;
https://doi.org/10.7910/DVN/LL3C1R</dcterms:relation><dcterms:license>CC0 1.0</dcterms:license></metadata>