COOS-7 (Cells Out Of Sample 7-Class)
Bibliographic record
Abstract
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. \n\nEach HDF5 file contains two main dictionaries:\n'data' - contains all of the images in a four-dimensional array (images, channels, height, width)\n'labels' - contains the labels for each image, in the same order as the images in 'data'\n\nNew in this version (1.1) - we have added four additional dictionaries containing metadata:\n'plateIDs' - string indicating the plate the image originated from\n'wellIDs' - string indicating the well the image originated from (first three numbers indicate row on plate, second three numbers indicate column on plate)\n'dateIDs' - string indicating date the image was taken on (YYYYMMDD)\n'microscopeIDs' - string indicating which microscope the image was taken on\n\nThe value for labels indicates the class of the image, which can be one of seven values:\n0 - Endoplasmic Reticulum (ER)\n1 - Inner Mitochondrial Membrane (IMM)\n2 - Golgi\n3 - Peroxisomes\n4 - Early Endosome\n5 - Cytosol\n6 - Nuclear Envelope\n\nThe h5py package is required to read these files with Python.\n\nWe 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.\n\nTo run the unpackaging script, issue the command line argument:\npython unpackage_COOS.py [path of HDF5 file] [path of directory to save images to]\ne.g. python unpackage_COOS.py ./COOS7_v1.0_training.hdf5 ./COOS7_v1.0_training_images/\n\nFull information about the test sets and the images can be found at https://arxiv.org/abs/1906.07282.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.104 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".