  Multi-class cell detection and counting is an essential task for many
pathological diagnoses. Manual counting is tedious and often leads to
inter-observer variations among pathologists. While there exist multiple,
general-purpose, deep learning-based object detection and counting methods,
they may not readily transfer to detecting and counting cells in medical
images, due to the limited data, presence of tiny overlapping objects, multiple
cell types, severe class-imbalance, minute differences in size/shape of cells,
etc. In response, we propose guided posterior regularization (DeGPR), which
assists an object detector by guiding it to exploit discriminative features
among cells. The features may be pathologist-provided or inferred directly from
visual data. We validate our model on two publicly available datasets (CoNSeP
and MoNuSAC), and on MuCeD, a novel dataset that we contribute. MuCeD consists
of 55 biopsy images of the human duodenum for predicting celiac disease. We
perform extensive experimentation with three object detection baselines on
three datasets to show that DeGPR is model-agnostic, and consistently improves
baselines obtaining up to 9% (absolute) mAP gains.
