COOS-7 (Cells Out Of Sample 7-Class)

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 main 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' New in this version (1.1) - we have added four additional dictionaries containing metadata: 'plateIDs' - string indicating the plate the image originated from 'wellIDs' - string indicating the well the image originated from (first three numbers indicate row on plate, second three numbers indicate column on plate) 'dateIDs' - string indicating date the image was taken on (YYYYMMDD) 'microscopeIDs' - string indicating which microscope the image was taken on 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.

本仓库包含COOS-7数据集的1.0版本（该数据集将作为海报在NeurIPS 2019会议展示；预印本详见https://arxiv.org/abs/1906.07282）。COOS-7共计包含132209张小鼠细胞裁剪图像，划分为训练集以及四个测试集，这四个测试集相较于训练集存在逐渐增强的协变量偏移（covariate shift）。针对COOS-7的分类任务，其目标是构建对显微镜成像中常见协变量偏移具有鲁棒性的分类器。方法开发者仅可使用训练集对机器学习模型进行训练与优化，并在四个测试集上分别评估模型性能。

每个HDF5文件包含两个核心字典：'data'以四维数组形式存储所有图像，维度依次为图像数量、通道数、高度与宽度；'labels'则按照'data'中图像的顺序，存储每张图像对应的标签。

本版本（1.1）新增了四个包含元数据的字典：
'plateIDs'——字符串类型，用于标识图像来源的培养板编号；
'wellIDs'——字符串类型，用于标识图像来源的孔位编号（前三位数字代表培养板的行，后三位数字代表培养板的列）；
'dateIDs'——字符串类型，用于标识图像拍摄日期，格式为YYYYMMDD；
'microscopeIDs'——字符串类型，用于标识拍摄图像所用的显微镜型号。

标签的取值代表图像所属类别，共7种可选值：
0 —— 内质网（Endoplasmic Reticulum, ER）
1 —— 线粒体内膜（Inner Mitochondrial Membrane, IMM）
2 —— 高尔基体（Golgi）
3 —— 过氧化物酶体（Peroxisomes）
4 —— 早期内体（Early Endosome）
5 —— 细胞质（Cytosol）
6 —— 核膜（Nuclear Envelope）

使用Python读取此类文件需安装h5py库。我们提供了Python脚本unpackage_COOS.py，可自动将归档文件解压为按类别组织的TIFF文件目录。每张图像的两个通道将分别保存为独立图像，后缀分别为"_protein.tif"与"_nucleus.tif"。运行该解压脚本的命令行参数格式为：python unpackage_COOS.py [HDF5文件路径] [图像保存目录路径]，示例如下：python unpackage_COOS.py ./COOS7_v1.0_training.hdf5 ./COOS7_v1.0_training_images/

关于测试集与图像的完整信息可参见https://arxiv.org/abs/1906.07282。

搜集汇总

背景与挑战

背景概述

COOS-7是一个包含132,209个小鼠细胞图像的数据集，用于评估机器学习模型在显微镜图像中的协变量偏移鲁棒性。数据集分为一个训练集和四个测试集，图像被标注为7个细胞器类别，并包含丰富的元数据，旨在支持模型在真实世界变异下的泛化能力研究。