Bespoke data augmentation and network construction enable image classification on small microscopy datasets
Recent work has indicated a need for increased temporal resolution for studies of the early chick brain. Over a 10-hour period, the developmental potential of progenitor cells in the HH10 brain changes, and concomitantly, the brain undergoes subtle changes in morphology. We asked if we could train a deep convolutional neural network to sub-stage HH10 brains from a small dataset (<200 images). By augmenting our images with a combination of biologically informed transformations and data-driven preprocessing steps, we successfully trained a classifier to sub-stage HH10 brains to 87.1% test accuracy. To determine whether our classifier could be generally applied, we re-trained it using images (<250) of randomised control and experimental chick wings, and obtained similarly high test accuracy (86.1%). Saliency analyses revealed that biologically relevant features are used for classification. Our strategy enables training of image classifiers for various applications in developmental biology with limited microscopy data. SUMMARY STATEMENTWe train a deep convolutional network that can be generally applied to accurately classify chick embryos from images. Saliency analyses show that classification is based on biologically relevant features.