Training Images for "ImmuNet" Convolutional Neural Network
Authors/Creators
- 1. Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands
- 2. Data Science group, Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands
- 3. Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
Description
This dataset contains annotations and annotated images for the "AgarCyto" samples used in this manuscript:
A Segmentation-Free Machine Learning Architecture for Immune Land-scape Phenotyping in Solid Tumors by Multichannel Imaging.
Shabaz Sultan, Mark A. J. Gorris, Lieke L. van der Woude, Franka Buytenhuijs, EvgeniaMartynova, Sandra van Wilpe, Kiek Verrijp, Carl G. Figdor, I. Jolanda M. de Vries, Johannes Textor
bioRxiv 2021.10.22.464548; doi: https://doi.org/10.1101/2021.10.22.464548
The .tar.gz file contains several multichannel images stored as TIFF files, and arranged in a folder structure that is convenient for matching the files to the annotations provided in the .json.gz file. We also provide an .h5 file that contains the final trained network that was used to generate the figures in this manuscript.
Further information on the data can be found in the manuscript cited above. Instructions on how to use the annotations and the code can be found on our GitHub page at: https://github.com/jtextor/immunet
Notes
Files
Files
(1.2 GB)
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md5:17ae2d0bcb7d758bd446bc805637c77b
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Additional details
Related works
- Is supplement to
- Preprint: 10.1101/2021.10.22.464548 (DOI)
- Software: https://github.com/jtextor/immunet (URL)