# Multi-Class Cell Detection Using Spatial Context Representation

arXiv: https://arxiv.org/abs/2110.04886 · HTML: https://mlsanity.com/paper/2110.04886/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 51 references that are in the map.
- First posted on arXiv 10 Oct 2021.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Weakly-Supervised Semantic Segmentation via Sub-category Exploration](https://mlsanity.com/paper/2008.01183/) (2020)
- [Localization in the Crowd with Topological Constraints](https://mlsanity.com/paper/2012.12482/) (2020)
- [Micro-Net: A unified model for segmentation of various objects in microscopy images](https://mlsanity.com/paper/1804.08145/) (2018)
- [CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation](https://mlsanity.com/paper/1903.05358/) (2019)
- [Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images](https://mlsanity.com/paper/2004.03037/) (2020)
- [Revisiting RCNN: On Awakening the Classification Power of Faster RCNN](https://mlsanity.com/paper/1803.06799/) (2018)
- [PseudoEdgeNet: Nuclei Segmentation only with Point Annotations](https://mlsanity.com/paper/1906.02924/) (2019)
- [DeepPap: Deep Convolutional Networks for Cervical Cell Classification](https://mlsanity.com/paper/1801.08616/) (2018)
- [Weakly Supervised Multi-Task Learning for Cell Detection and Segmentation](https://mlsanity.com/paper/1910.12326/) (2019)

## Built on by (most cited first, 24 of 24)

- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images](https://mlsanity.com/paper/2306.07831/) (2023)
- [ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis](https://mlsanity.com/paper/2308.13324/) (2023)
- [DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting](https://mlsanity.com/paper/2304.00741/) (2023)
- [TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model](https://mlsanity.com/paper/2412.06011/) (2024)
- [Structure Embedded Nucleus Classification for Histopathology Images](https://mlsanity.com/paper/2302.11416/) (2023)
- [Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection](https://mlsanity.com/paper/2310.14154/) (2023)
- [DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detection](https://mlsanity.com/paper/2303.02602/) (2023)
- [Topology-Guided Multi-Class Cell Context Generation for Digital Pathology](https://mlsanity.com/paper/2304.02255/) (2023)
- [Spatial Diffusion for Cell Layout Generation](https://mlsanity.com/paper/2409.03106/) (2024)
- [Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images](https://mlsanity.com/paper/2208.07322/) (2022)
- [Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View](https://mlsanity.com/paper/2407.12870/) (2024)
- [RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency](https://mlsanity.com/paper/2411.15076/) (2024)
- [DCA: Graph-Guided Deep Embedding Clustering for Brain Atlases](https://mlsanity.com/paper/2509.01426/) (2025)
- [COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation](https://mlsanity.com/paper/2503.11439/) (2025)
- [NuNext: Reframing Nucleus Detection as Next-Point Detection](https://mlsanity.com/paper/2603.07098/) (2026)
- [Prompt-based Grouping Transformer for Nucleus Detection and Classification](https://mlsanity.com/paper/2310.14176/) (2023)
- [BOrg: A Brain Organoid-Based Mitosis Dataset for Automatic Analysis of Brain Diseases](https://mlsanity.com/paper/2406.19556/) (2024)
- [DeNuC: Decoupling Nuclei Detection and Classification in Histopathology](https://mlsanity.com/paper/2603.04240/) (2026)
- [Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy](https://mlsanity.com/paper/2606.12346/) (2026)
- [Cell as Point: One-Stage Framework for Efficient Cell Tracking](https://mlsanity.com/paper/2411.14833/) (2024)
- [Patherea: Cell Detection and Classification for the 2020s](https://mlsanity.com/paper/2412.16425/) (2024)
- [DualGate-Net: A Prior-Gated Dual-Encoder Framework for Histopathology Cell Detection](https://mlsanity.com/paper/2606.07222/) (2026)
- [End-to-End Cell Detection via Instance-aware Graph Modeling](https://mlsanity.com/paper/2609.15354/) (2026)

## Abstract (arXiv)

In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks. Classifying cells into subtypes, such as tumor cells, lymphocytes or stromal cells is particularly challenging. Existing methods focus on morphological appearance of individual cells, whereas in practice pathologists often infer cell classes through their spatial context. In this paper, we propose a novel method for both detection and classification that explicitly incorporates spatial contextual information. We use the spatial statistical function to describe local density in both a multi-class and a multi-scale manner. Through representation learning and deep clustering techniques, we learn advanced cell representation with both appearance and spatial context. On various benchmarks, our method achieves better performance than state-of-the-arts, especially on the classification task. We also create a new dataset for multi-class cell detection and classification in breast cancer and we make both our code and data publicly available.