Motivation: In recent years, image-based biological assays have steadily become high-throughput, sparking a need for fast automated methods to extract biologically-meaningful information from hundreds of thousands of images. Taking inspiration from the success of ImageNet, we curate CytoImageNet, a large-scale dataset of openly-sourced and weakly-labeled microscopy images (890K images, 894 classes). Pretraining on CytoImageNet yields features that are competitive to ImageNet features on downstream microscopy classification tasks. We show evidence that CytoImageNet features capture information not available in ImageNet-trained features. The dataset is made available at https://www.kaggle.com/stanleyhua/cytoimagenet.
@article{arxiv.2111.11646,
title = {CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning},
author = {Stanley Bryan Z. Hua and Alex X. Lu and Alan M. Moses},
journal= {arXiv preprint arXiv:2111.11646},
year = {2021}
}
Comments
Accepted paper at NeurIPS 2021 Learning Meaningful Representations for Life (LMRL) Workshop