DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting
This addresses the tedious and variable manual cell counting in pathological diagnoses, offering a model-agnostic improvement, though it is incremental as it builds on existing object detection methods.
The paper tackled the problem of multi-class cell detection and counting in medical images, where existing methods struggle due to issues like limited data and overlapping objects, and proposed DeGPR, which improved baseline object detectors by up to 9% absolute mAP gains on datasets like CoNSeP and MoNuSAC.
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.