# Structure Embedded Nucleus Classification for Histopathology Images

arXiv: https://arxiv.org/abs/2302.11416 · HTML: https://mlsanity.com/paper/2302.11416/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 77 references that are in the map.
- First posted on arXiv 22 Feb 2023.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [DeeperGCN: All You Need to Train Deeper GCNs](https://mlsanity.com/paper/2006.07739/) (2020)
- [Visual Attention Network](https://mlsanity.com/paper/2202.09741/) (2022)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis](https://mlsanity.com/paper/1912.08937/) (2019)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [Annotating Object Instances with a Polygon-RNN](https://mlsanity.com/paper/1704.05548/) (2017)
- [DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation](https://mlsanity.com/paper/1604.02677/) (2016)
- [A graph-transformer for whole slide image classification](https://mlsanity.com/paper/2205.09671/) (2022)
- [DeepGCNs: Making GCNs Go as Deep as CNNs](https://mlsanity.com/paper/1910.06849/) (2019)
- [Hierarchical Graph Representations in Digital Pathology](https://mlsanity.com/paper/2102.11057/) (2021)
- [CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images](https://mlsanity.com/paper/1909.01068/) (2019)
- [Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images](https://mlsanity.com/paper/1810.00236/) (2018)
- [CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation](https://mlsanity.com/paper/1903.05358/) (2019)
- [One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification](https://mlsanity.com/paper/2203.00077/) (2022)
- [Quantifying Explainers of Graph Neural Networks in Computational Pathology](https://mlsanity.com/paper/2011.12646/) (2020)
- [CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation](https://mlsanity.com/paper/2102.06867/) (2021)
- [Multi-Class Cell Detection Using Spatial Context Representation](https://mlsanity.com/paper/2110.04886/) (2021)
- [DeepPap: Deep Convolutional Networks for Cervical Cell Classification](https://mlsanity.com/paper/1801.08616/) (2018)
- [Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs](https://mlsanity.com/paper/2103.03129/) (2021)
- [SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation](https://mlsanity.com/paper/2106.01544/) (2021)
- [Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks](https://mlsanity.com/paper/1910.13328/) (2019)
- [Boundary-assisted Region Proposal Networks for Nucleus Segmentation](https://mlsanity.com/paper/2006.02695/) (2020)

## Built on by (most cited first, 14 of 14)

- [Unleashing the Power of Prompt-driven Nucleus Instance Segmentation](https://mlsanity.com/paper/2311.15939/) (2023)
- [Cell Graph Transformer for Nuclei Classification](https://mlsanity.com/paper/2402.12946/) (2024)
- [Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images](https://mlsanity.com/paper/2503.05678/) (2025)
- [OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides](https://mlsanity.com/paper/2407.12773/) (2024)
- [Category Prompt Mamba Network for Nuclei Segmentation and Classification](https://mlsanity.com/paper/2503.10422/) (2025)
- [MUSE: Multi-Scale Dense Self-Distillation for Nucleus Detection and Classification](https://mlsanity.com/paper/2511.05170/) (2025)
- [UniCell: Universal Cell Nucleus Classification via Prompt Learning](https://mlsanity.com/paper/2402.12938/) (2024)
- [Learning Spatial-Preserving Hierarchical Representations for Digital Pathology](https://mlsanity.com/paper/2406.09333/) (2024)
- [DeNuC: Decoupling Nuclei Detection and Classification in Histopathology](https://mlsanity.com/paper/2603.04240/) (2026)
- [Instance-Aware Robust Consistency Regularization for Semi-Supervised Nuclei Instance Segmentation](https://mlsanity.com/paper/2510.09329/) (2025)
- [LG-NuSegHop: A Local-to-Global Self-Supervised Pipeline For Nuclei Instance Segmentation](https://mlsanity.com/paper/2511.04892/) (2025)
- [Adaptive Multi-Scale Integration Unlocks Robust Cell Annotation in Histopathology Images](https://mlsanity.com/paper/2511.13586/) (2025)
- [Context-aware Skin Cancer Epithelial Cell Classification with Scalable Graph Transformers](https://mlsanity.com/paper/2602.15783/) (2026)
- [End-to-End Cell Detection via Instance-aware Graph Modeling](https://mlsanity.com/paper/2609.15354/) (2026)

## Abstract (arXiv)

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the state-of-the-art methods.