Dataset from "Identifying zebrafish segmentation phenotype features using multiple instance learning"
Vojislav Gligorovski, Rachna Narayanan, Weigert, Martin · Zenodo (CERN European Organization for Nuclear Research) · 2022
The dataset was used to produce the results from the "Identifying zebrafish segmentation phenotype features using multiple instance learning" manuscript. It contains images of zebrafish embryos obtained by in situ hybridization that were used to train and evaluate the performance of different neural network-based image classifiers. Images were split into 4 classes: unmodified (WT) zebrafish embryo, and 3 more phenotypes reflecting different segmentation clock defects. Folder 'data' contains 3 different directories: 'training', the set used for training of the classifiers, 'validation', the set used for evaluation of the performance of the classifiers, with 20 images from each class, and 'fish_part_labels' that contains the annotation of zebrafish embryo parts (head, trunk, tail, yolk, and yolk extension) of the images from the 'validation' set.