  Automatic detection of lymphocyte in H&E images is a necessary first step in
lots of tissue image analysis algorithms. An accurate and robust automated
lymphocyte detection approach is of great importance in both computer science
and clinical studies. Most of the existing approaches for lymphocyte detection
are based on traditional image processing algorithms and/or classic machine
learning methods. In the recent years, deep learning techniques have
fundamentally transformed the way that a computer interprets images and have
become a matchless solution in various pattern recognition problems. In this
work, we design a new deep neural network model which extends the fully
convolutional network by combining the ideas in several recent techniques, such
as shortcut links. Also, we design a new training scheme taking the prior
knowledge about lymphocytes into consideration. The training scheme not only
efficiently exploits the limited amount of free-form annotations from
pathologists, but also naturally supports efficient fine-tuning. As a
consequence, our model has the potential of self-improvement by leveraging the
errors collected during real applications. Our experiments show that our deep
neural network model achieves good performance in the images of different
staining conditions or different types of tissues.
