  This paper presents a new deep regression model, which we call DeepDistance,
for cell detection in images acquired with inverted microscopy. This model
considers cell detection as a task of finding most probable locations that
suggest cell centers in an image. It represents this main task with a
regression task of learning an inner distance metric. However, different than
the previously reported regression based methods, the DeepDistance model
proposes to approach its learning as a multi-task regression problem where
multiple tasks are learned by using shared feature representations. To this
end, it defines a secondary metric, normalized outer distance, to represent a
different aspect of the problem and proposes to define its learning as
complementary to the main cell detection task. In order to learn these two
complementary tasks more effectively, the DeepDistance model designs a fully
convolutional network (FCN) with a shared encoder path and end-to-end trains
this FCN to concurrently learn the tasks in parallel. DeepDistance uses the
inner distances estimated by this FCN in a detection algorithm to locate
individual cells in a given image. For further performance improvement on the
main task, this paper also presents an extended version of the DeepDistance
model. This extended model includes an auxiliary classification task and learns
it in parallel to the two regression tasks by sharing feature representations
with them. Our experiments on three different human cell lines reveal that the
proposed multi-task learning models, the DeepDistance model and its extended
version, successfully identify cell locations, even for the cell line that was
not used in training, and improve the results of the previous deep learning
methods.
