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

arXiv: https://arxiv.org/abs/2304.00741 · HTML: https://mlsanity.com/paper/2304.00741/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 54 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Sequence Level Training with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06732/) (2015)
- [Understanding the Behaviour of Contrastive Loss](https://mlsanity.com/paper/2012.09740/) (2020)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [Detecting and classifying lesions in mammograms with Deep Learning](https://mlsanity.com/paper/1707.08401/) (2017)
- [Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection](https://mlsanity.com/paper/1811.08661/) (2018)
- [Count-ception: Counting by Fully Convolutional Redundant Counting](https://mlsanity.com/paper/1703.08710/) (2017)
- [Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides](https://mlsanity.com/paper/1811.08513/) (2018)
- [Multi-Class Cell Detection Using Spatial Context Representation](https://mlsanity.com/paper/2110.04886/) (2021)
- [Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection](https://mlsanity.com/paper/1901.07061/) (2019)
- [Target-Independent Domain Adaptation for WBC Classification using Generative Latent Search](https://mlsanity.com/paper/2005.05432/) (2020)

## Built on by (most cited first, 18 of 18)

- [Zero-shot Object Counting with Good Exemplars](https://mlsanity.com/paper/2407.04948/) (2024)
- [T2ICount: Enhancing Cross-modal Understanding for Zero-Shot Counting](https://mlsanity.com/paper/2502.20625/) (2025)
- [Single Domain Generalization for Few-Shot Counting via Universal Representation Matching](https://mlsanity.com/paper/2505.16778/) (2025)
- [[De|Re]constructing VLMs' Reasoning in Counting](https://mlsanity.com/paper/2510.19555/) (2025)
- [Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View](https://mlsanity.com/paper/2407.12870/) (2024)
- [LSM-YOLO: A Compact and Effective ROI Detector for Medical Detection](https://mlsanity.com/paper/2408.14087/) (2024)
- [TFCounter:Polishing Gems for Training-Free Object Counting](https://mlsanity.com/paper/2405.02301/) (2024)
- [Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models](https://mlsanity.com/paper/2505.16647/) (2025)
- [BoxCell: Leveraging SAM for Cell Segmentation with Box Supervision](https://mlsanity.com/paper/2311.17960/) (2023)
- [BOrg: A Brain Organoid-Based Mitosis Dataset for Automatic Analysis of Brain Diseases](https://mlsanity.com/paper/2406.19556/) (2024)
- [MeasureNet: Measurement Based Celiac Disease Identification](https://mlsanity.com/paper/2412.01182/) (2024)
- [Expanding Zero-Shot Object Counting with Rich Prompts](https://mlsanity.com/paper/2505.15398/) (2025)
- [AdaCount: Training-Free Similarity-Guided Spatial and Feature Adaptation for Zero-Shot Object Counting](https://mlsanity.com/paper/2607.02139/) (2026)
- [Text-guided Zero-Shot Object Localization](https://mlsanity.com/paper/2411.11357/) (2024)
- [FocalCount: Towards Class-Count Imbalance in Class-Agnostic Counting](https://mlsanity.com/paper/2502.10677/) (2025)
- [CountZES: Counting via Zero-Shot Exemplar Selection](https://mlsanity.com/paper/2512.16415/) (2025)
- [Compositional Reward Models for Conditional Medical Image Generation](https://mlsanity.com/paper/2609.05028/) (2026)
- [DualCount: Structurally Consistent Density and Point Modeling for Zero-Shot Object Counting](https://mlsanity.com/paper/2609.17613/) (2026)

## Abstract (arXiv)

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.