We introduce spatially informed variational autoencoders (SI-VAE) as self-supervised deep-learning models that use stochastic point processes to predict spatial organization patterns from images....

We propose a method for variable selection in the intensity function of spatial point processes that combines sparsity-promoting estimation with noise-robust model selection. As high-resolution spatia...

We present APR-CNN, a novel class of convolutional neural networks designed for efficient and scalable three-dimensional microscopy image analysis. APR-CNNs operate natively on a sparse...