  Automatic cell detection in histology images is a challenging task due to
varying size, shape and features of cells and stain variations across a large
cohort. Conventional deep learning methods regress the probability of each
pixel belonging to the centre of a cell followed by detection of local maxima.
We present deconvolution as an alternate approach to local maxima detection.
The ground truth points are convolved with a mapping filter to generate
artifical labels. A convolutional neural network (CNN) is modified to convolve
it's output with the same mapping filter and is trained for the mapped labels.
Output of the trained CNN is then deconvolved to generate points as cell
detection. We compare our method with state-of-the-art deep learning approaches
where the results show that the proposed approach detects cells with
comparatively high precision and F1-score.
