Tackling the challenges of bioimage analysis

Abstract

Using multiple human annotators and ensembles of trained networks can improve the performance of deep-learning methods in research.

Keywords: bioimage informatics; computational biology; deep learning; fluorescence microscopy; mouse; neuroscience; objectivity; reproducibility; systems biology; validity; zebrafish.

Figure 1.. Different ways to train a convolutional neural network.

Segebarth et al. compare three techniques for training convolutional neural networks to analyze bioimages. (A) In the standard approach a single human expert annotates images for training a single network. (B) In a second approach multiple human experts annotate the same images, and consensus images are used for training: this improves the objectivity of the trained network. (C) In a third approach, a technique called model ensembling is added to the second approach, meaning that multiple networks are trained with the same consensus images: this improves the reliability of the results.