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

• URL: http://arxiv.org/abs/2304.00741v1
• Date: Mon, 3 Apr 2023 06:25:45 GMT
• Title: DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting
• Authors: Aayush Kumar Tyagi, Chirag Mohapatra, Prasenjit Das, Govind Makharia, Lalita Mehra, Prathosh AP, Mausam
• Abstract summary: Multi-class cell detection and counting is an essential task for many pathological diagnoses. We propose guided posterior regularization (DeGPR) which assists an object detector by guiding it to exploit discriminative features among cells. We validate our model on two publicly available datasets, and on MuCeD, a novel dataset that we contribute.
• Score: 14.222014969736993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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