OrgaNet: A Robust Network for Subcellular Organelles Classification in Fluorescence Microscopy Images

Automatic identification of subcellular compartments of proteins in fluorescence microscopy images is an important task to quantitatively evaluate cellular processes. A common problem for the development of deep learning based classifiers is that there is only a limited number of labeled images available for training. To address this challenge, we propose a new approach for subcellular organelles classification combining an effective and efficient architecture based on a compact Convolutional Neural Network and deep embedded clustering algorithm. We validate our approach on a benchmark of HeLa cell microscopy images. The network both yields high accuracy that outperforms state of the art methods and has significantly small number of parameters. More interestingly, experimental results show that our method is strongly robust against limited labeled data for training, requiring four times less annotated data than usual while maintaining the high accuracy of 93.9%.