Learning with minimal effort: leveraging ISL for segmentation

Anonymous

Keywords: Segmentation, Transfer learning, Pretext task, In Silico Labeling, Fluorescence microscopy

TL;DR: Use In Silico Labeling (ISL), i.e. predicting fluorescently labeled images from the label-free microscopy images, as a pretraining scheme for segmentation tasks.

Abstract: Deep learning provides us with powerful methods to perform nucleus or cell segmentation with unprecedented quality. However, these methods usually require large training sets of manually annotated images, which are tedious --- and expensive --- to generate. In this paper we propose to use In Silico Labeling (ISL) as a pretraining scheme for segmentation tasks. The strategy is to acquire label-free microscopy images (such as bright-field or phase contrast) along fluorescently labeled images (such as DAPI or CellMask\texttrademark). We then train a model to predict the fluorescently labeled images from the label-free microscopy images. By comparing segmentation performance across several training set sizes, we show that such a scheme can dramatically reduce the number of required annotations.