  Cell detection and segmentation is fundamental for all downstream analysis of
digital pathology images. However, obtaining the pixel-level ground truth for
single cell segmentation is extremely labor intensive. To overcome this
challenge, we developed an end-to-end deep learning algorithm to perform both
single cell detection and segmentation using only point labels. This is
achieved through the combination of different task orientated point label
encoding methods and a multi-task scheduler for training. We apply and validate
our algorithm on PMS2 stained colon rectal cancer and tonsil tissue images.
Compared to the state-of-the-art, our algorithm shows significant improvement
in cell detection and segmentation without increasing the annotation efforts.
