mlsanityData to 14 Oct 2026

Multi-Class Cell Detection Using Spatial Context Representation

arXiv 2110.04886 ↗First posted 10 Oct 2021cs.CVcited by 24

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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.

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

19 of 51 references.

References · 19

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  4. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,276
  5. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
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  8. 2018Invariant Information Clustering for Unsupervised Image Classification and Segmentationcited by 455
  9. 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 429
  10. 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 388
  11. 2020Weakly-Supervised Semantic Segmentation via Sub-category Explorationcited by 145
  12. 2020Localization in the Crowd with Topological Constraintscited by 68
7 more
  1. 2018Micro-Net: A unified model for segmentation of various objects in microscopy imagescited by 56
  2. 2019CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregationcited by 54
  3. 2020Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Imagescited by 33
  4. 2018Revisiting RCNN: On Awakening the Classification Power of Faster RCNNcited by 28
  5. 2019PseudoEdgeNet: Nuclei Segmentation only with Point Annotationscited by 27
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 8; 2024: 7; 2025: 2; 2026: 5.

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  1. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  2. 2023Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Imagescited by 134
  3. 2023ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysiscited by 25
  4. 2023DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Countingcited by 18
  5. 2024TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Modelcited by 16
  6. 2023Structure Embedded Nucleus Classification for Histopathology Imagescited by 14
  7. 2023Affine-Consistent Transformer for Multi-Class Cell Nuclei Detectioncited by 12
  8. 2023DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detectioncited by 8
  9. 2023Topology-Guided Multi-Class Cell Context Generation for Digital Pathologycited by 7
  10. 2024Spatial Diffusion for Cell Layout Generationcited by 5
  11. 2022Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Imagescited by 4
  12. 2024Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence Viewcited by 4
12 more
  1. 2024RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistencycited by 4
  2. 2025DCA: Graph-Guided Deep Embedding Clustering for Brain Atlasescited by 4
  3. 2025COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentationcited by 3
  4. 2026NuNext: Reframing Nucleus Detection as Next-Point Detectioncited by 2
  5. 2023Prompt-based Grouping Transformer for Nucleus Detection and Classificationcited by 1
  6. 2024BOrg: A Brain Organoid-Based Mitosis Dataset for Automatic Analysis of Brain Diseasescited by 1
  7. 2026DeNuC: Decoupling Nuclei Detection and Classification in Histopathologycited by 1
  8. 2026Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracycited by 1
  9. 2024Cell as Point: One-Stage Framework for Efficient Cell Trackingcited by 0
  10. 2024Patherea: Cell Detection and Classification for the 2020scited by 0
  11. 2026DualGate-Net: A Prior-Gated Dual-Encoder Framework for Histopathology Cell Detectioncited by 0
  12. 2026End-to-End Cell Detection via Instance-aware Graph Modelingcited by 0