  We propose a machine learning approach to the blind detection of
extragalactic point sources on maps of the temperature anisotropies of the
cosmic microwave background. Using realistic simulations of the microwave sky
as seen by Planck, we train a convolutional neural network (CNN) that solves
source detection as an image segmentation problem. We divide the sky into
regions of progressively increasing Galactic foreground intensity and
independently train specialized CNNs for each region. This strategy leads to
promising levels of completeness and reliability, with our CNN substantially
outperforming traditional detection methods like the matched filter in regions
close to the Galactic plane.
