  Methods for source detection in high noise environments are important for
single-photon emission computed tomography (SPECT) medical imaging and
especially crucial for homeland security applications, which is our main
interest. In the latter case, one deals with passively detecting the presence
of low emission nuclear sources with significant background noise (with Signal
To Noise Ratio ($SNR$) $1\%$ or less). In passive emission problems, direction
sensitive detectors are needed, to match the dimensionalities of the image and
the data. Collimation, used for that purpose in standard Anger
$\gamma$-cameras, is not an option. Instead, Compton $\gamma$-cameras (and
their analogs for other types of radiation) can be utilized. Backprojection
methods suggested before by two of the authors and their collaborators enable
detection in the presence of a random uniform background. In most practical
applications, however, cargo packing in shipping containers and trucks creates
regions of strong absorption and scattering, while leaving some streaming gaps
open. In such cases backprojection methods prove ineffective and lose their
detection ability. Nonetheless, visual perception of the backprojection
pictures suggested that some indications of presence of a source might still be
in the data. To learn such features (if they do exist), a deep neural network
approach is implemented in 2D, which indeed exhibits higher sensitivity and
specificity than the backprojection techniques in a low scattering case and
works well when presence of complex cargo makes backprojection fail completely.
