DEGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting

Aayush Kumar Tyagi

Abstract:

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 meth-
ods, 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 vi-
sual data. We validate our model on two publicly avail-
able 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 base-
lines obtaining up to 9% (absolute) mAP gains.