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Part 1: Document Description
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Citation |
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Title: |
AIMOS - pre-trained models |
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Identification Number: |
doi:10.7910/DVN/G6VLZN |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2020-04-28 |
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Version: |
1 |
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Bibliographic Citation: |
Schoppe, Oliver, 2020, "AIMOS - pre-trained models", https://doi.org/10.7910/DVN/G6VLZN, Harvard Dataverse, V1 |
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Citation |
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Title: |
AIMOS - pre-trained models |
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Identification Number: |
doi:10.7910/DVN/G6VLZN |
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Authoring Entity: |
Schoppe, Oliver (Technische Universität München (TUM)) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Schoppe, Oliver |
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Depositor: |
Schoppe, Oliver |
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Date of Deposit: |
2020-04-27 |
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Holdings Information: |
https://doi.org/10.7910/DVN/G6VLZN |
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Study Scope |
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Keywords: |
Computer and Information Science, Medicine, Health and Life Sciences |
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Abstract: |
Pre-trained models for AIMOS * Unet768 for native micro-CT * Unet768 for contrast-enhanced micro-CT * Unet768 for light-sheet microscopy |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Studies |
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https://www.nature.com/articles/sdata2018294, https://doi.org/10.7910/DVN/LL3C1R |
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Label: |
CECTmodel.pt |
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Notes: |
application/octet-stream |
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Label: |
LSFMmodel.pt |
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Notes: |
application/octet-stream |
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Label: |
NACTmodel.pt |
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Notes: |
application/octet-stream |