Microscopy is ubiquitous in biological research, and with high content screening
there is a need to analyze images at scale. High content screening often uses
multichannel, epifluorescence microscopy (multiplexing), and fluorescent images often
exhibit channel mixing, or bleed-through effects, which need to be corrected before
subsequent analysis (e.g. segmentation, feature extraction, etc). In this paper we present
Theia, an algorithm for bleed-through correction that requires little to no
\textit{a priori} information about the source or content of the images (i.e. number
of channels). Theia uses a novel neural network architecture inspired by Siamese
Networks and Least Absolute Shrinkage and Selection Operator (LASSO) regression to
learn convolutional filters that remove bleed-through. We use metrics for quantifying
bleed-through, and show Theia exhibits good capacity for removing bleed-through on
both synthetic and real fluorescent images. Theia was benchmarked to demonstrate
scalability across diverse datasets with varying degrees of bleed-through and numbers
of channels. Since Theia learns a set of convolutional kernels using popular neural
network frameworks, it can make use of GPU acceleration when scaling to large datasets.