  For any type of microscopy image, getting a deep learning model to work well
requires considerable effort to select a suitable architecture and time to
train it. As there is a wide range of microscopes and experimental setups,
designing a single model that can apply to multiple imaging domains, instead of
having multiple per-domain models, becomes more essential. This task is
challenging and somehow overlooked in the literature. In this paper, we present
a multi-domain learning architecture for the classification of microscopy
images that differ significantly in types and contents. Unlike previous methods
that are computationally intensive, we have developed a compact model, called
Mobincep, by combining the simple but effective techniques of depth-wise
separable convolution and the inception module. We also introduce a new
optimization technique to regulate the latent feature space during training to
improve the network's performance. We evaluated our model on three different
public datasets and compared its performance in single-domain and
multiple-domain learning modes. The proposed classifier surpasses
state-of-the-art results and is robust for limited labeled data. Moreover, it
helps to eliminate the burden of designing a new network when switching to new
experiments.
