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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp013r074x85b
Title: Fluorescence Reconstruction Microscopy: Complete Testing Dataset
Contributors: LaChance, Julienne
Cohen, Daniel
Keywords: machine learning
training
fluorescence reconstruction
FRM
Issue Date: Apr-2020
Publisher: Princeton University
Abstract: We provide all the test data and corresponding predictions for our paper, “Practical Fluorescence Reconstruction Microscopy for High-Content Imaging”. Please refer to the Methods section in this paper for experimental details. For each experimental condition, we provide the input transmitted-light images (either phase contrast or DIC), the ground truth fluorescence images, and the output predicted fluorescence images which should reconstruct the ground truth fluorescence images.
URI: http://arks.princeton.edu/ark:/88435/dsp013r074x85b
https://www.dropbox.com/sh/l3jb5ts6iow4daj/AAC_AaEAPykInyhFxG3fINdva?dl=0
https://www.dropbox.com/sh/2r2qm0awr8pahlr/AAAsvBvMtJPX_nqKyaodvuBZa?dl=0
https://www.dropbox.com/sh/g0kby5n93ftul0j/AADpLa5vhlJZIwXIgm9lpG_Wa?dl=0
https://www.dropbox.com/sh/l1d1eirhybmwtx0/AADJU3ZV1JKP6QNqX2jQJ0aba?dl=0
https://doi.org/10.34770/s59n-wm23
Appears in Collections:MAE Research Data Sets

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DataSpace_Full_Data_Details_README.pdfREADME documentation for the dataset62.38 kBAdobe PDFView/Download


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