  Accurate cell detection and counting in the image-based ELISpot and
FluoroSpot immunoassays is a challenging task. Recently proposed methodology
matches human accuracy by leveraging knowledge of the underlying physical
process of these assays and using proximal optimization methods to solve an
inverse problem. Nonetheless, thousands of computationally expensive iterations
are often needed to reach a near-optimal solution. In this paper, we exploit
the structure of the iterations to design a parameterized computation graph,
SpotNet, that learns the patterns embedded within several training images and
their respective cell information. Further, we compare SpotNet to a
convolutional neural network layout customized for cell detection. We show
empirical evidence that, while both designs obtain a detection performance on
synthetic data far beyond that of a human expert, SpotNet is easier to train
and obtains better estimates of particle secretion for each cell.
