  Multiple stains are usually used to highlight biological substances in
biomedical image analysis. To decompose multiple stains for co-localization
quantification, blind source separation is usually performed. Prior model-based
stain separation methods usually rely on stains' spatial distributions over an
image and may fail to solve the co-localization problem. With the advantage of
machine learning, deep generative models are used for this purpose. Since prior
knowledge of imaging models is ignored in purely data-driven solutions, these
methods may be sub-optimal. In this study, a novel learning-based blind source
separation framework is proposed, where the physical model of biomedical
imaging is incorporated to regularize the learning process. The introduced
model-relevant adversarial loss couples all generators in the framework and
limits the capacities of the generative models. Further more, a training
algorithm is innovated for the proposed framework to avoid inter-generator
confusion during learning. This paper particularly takes fluorescence unmixing
in fluorescence microscopy images as an application example of the proposed
framework. Qualitative and quantitative experimentation on a public
fluorescence microscopy image set demonstrates the superiority of the proposed
method over both prior model-based approaches and learning-based methods.
