  Accurately counting cells in microscopic images is important for medical
diagnoses and biological studies, but manual cell counting is very tedious,
time-consuming, and prone to subjective errors, and automatic counting can be
less accurate than desired. To improve the accuracy of automatic cell counting,
we propose here a novel method that employs deeply-supervised density
regression. A fully convolutional neural network (FCNN) serves as the primary
FCNN for density map regression. Innovatively, a set of auxiliary FCNNs are
employed to provide additional supervision for learning the intermediate layers
of the primary CNN to improve network performance. In addition, the primary CNN
is designed as a concatenating framework to integrate multi-scale features
through shortcut connections in the network, which improves the granularity of
the features extracted from the intermediate CNN layers and further supports
the final density map estimation.
