Multi-domain learning CNN model for microscopy image classification

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.