Dataset Open Access
This repository contains the version 1.0 of the COOS-7 dataset (to be presented as a poster at NeurIPS 2019; see preprint at https://arxiv.org/abs/1906.07282). COOS-7 contains 132,209 crops of mouse cells, stratified into a training dataset, and four test datasets representing increasing degrees of covariate shift from the training dataset. In the classification task associated with COOS-7, the aim is to build a classifier robust to covariate shifts typically seen in microscopy. Methods developers must train and optimize machine learning models using the training dataset exclusively, and evaluate performance on each of the four test datasets.
Each HDF5 file contains two dictionaries:
'data' - contains all of the images in a four-dimensional array (images, channels, height, width)
'labels' - contains the labels for each image, in the same order as the images in 'data'
The value for labels indicates the class of the image, which can be one of seven values:
0 - Endoplasmic Reticulum (ER)
1 - Inner Mitochondrial Membrane (IMM)
2 - Golgi
3 - Peroxisomes
4 - Early Endosome
5 - Cytosol
6 - Nuclear Envelope
The h5py package is required to read these files with Python.
We provide a Python script, unpackage_COOS.py, that will automatically save the archives as directories of tiff files, organized by class. The two channels for each image will be saved as separate images, with a suffix of "_protein.tif" and "_nucleus.tif", respectively.
To run the unpackaging script, issue the command line argument:
python unpackage_COOS.py [path of HDF5 file] [path of directory to save images to]
e.g. python unpackage_COOS.py ./COOS7_v1.0_training.hdf5 ./COOS7_v1.0_training_images/
Full information about the test sets and the images can be found at https://arxiv.org/abs/1906.07282.