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Accurate cell detection and counting in the image-based ELISpot andFluoroSpot immunoassays is a challenging task. Recently proposed methodologymatches human accuracy by leveraging knowledge of the underlying physicalprocess of these assays and using proximal optimization methods to solve aninverse problem. Nonetheless, thousands of computationally expensive iterationsare often needed to reach a near-optimal solution. In this paper, we exploitthe structure of the iterations to design a parameterized computation graph,SpotNet, that learns the patterns embedded within several training images andtheir respective cell information. Further, we compare SpotNet to aconvolutional neural network layout customized for cell detection. We showempirical evidence that, while both designs obtain a detection performance onsynthetic data far beyond that of a human expert, SpotNet is easier to trainand obtains better estimates of particle secretion for each cell.

SpotNet - Learned iterations for cell detection in image-based immunoassays