mlsanityData to 7 Oct 2026

HistoCartography: A Toolkit for Graph Analytics in Digital Pathology

arXiv 2107.10073 ↗First posted 21 Jul 2021eess.IV cs.CVcited by 20

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Abstract (arXiv)

Advances in entity-graph based analysis of histopathology images have brought in a new paradigm to describe tissue composition, and learn the tissue structure-to-function relationship. Entity-graphs offer flexible and scalable representations to characterize tissue organization, while allowing the incorporation of prior pathological knowledge to further support model interpretability and explainability. However, entity-graph analysis requires prerequisites for image-to-graph translation and knowledge of state-of-the-art machine learning algorithms applied to graph-structured data, which can potentially hinder their adoption. In this work, we aim to alleviate these issues by developing HistoCartography, a standardized python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology. Further, we have benchmarked the computational time and performance on multiple datasets across different imaging types and histopathology tasks to highlight the applicability of the API for building computational pathology workflows.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 29 of its 54 references that are in the map. First posted on arXiv 21 Jul 2021.

Builds on

29 of 54 references.

References · 29

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17 more
  1. 2018BACH: Grand Challenge on Breast Cancer Histology Imagescited by 131
  2. 2020PanNuke Dataset Extension, Insights and Baselinescited by 113
  3. 2021Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detectioncited by 66
  4. 2018Neural Image Compression for Gigapixel Histopathology Image Analysiscited by 65
  5. 2021Hierarchical Graph Representations in Digital Pathologycited by 58
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  7. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  8. 2020Representation Learning of Histopathology Images using Graph Neural Networkscited by 40
  9. 2019Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Imagescited by 39
  10. 2020Quantifying Explainers of Graph Neural Networks in Computational Pathologycited by 35
  11. 2020HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classificationcited by 33
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  13. 2021Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphscited by 22
  14. 2019Histographs: Graphs in Histopathologycited by 20
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 7; 2024: 4; 2025: 0; 2026: 3.

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  1. 2023Artificial Intelligence for Digital and Computational Pathologycited by 106
  2. 2024GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AIcited by 96
  3. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  4. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  5. 2021SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancercited by 31
  6. 2022A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protectioncited by 20
  7. 2024Generative Modelling of Structurally Constrained Graphscited by 17
  8. 2022Explainable AI for Bioinformatics: Methods, Tools, and Applicationscited by 11
  9. 2023Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancercited by 10
  10. 2021Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flowscited by 4
  11. 2023HoVer-UNet: Accelerating HoVerNet with UNet-based multi-class nuclei segmentation via knowledge distillationcited by 4
  12. 2023Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosiscited by 2
8 more
  1. 2023Tertiary Lymphoid Structures Generation through Graph-based Diffusioncited by 2
  2. 2023KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology imagescited by 1
  3. 2024Integrating multiscale topology in digital pathology with pyramidal graph convolutional networkscited by 1
  4. 2024Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answeringcited by 1
  5. 2026PathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classificationcited by 1
  6. 2023PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labelingcited by 0
  7. 2026Context-aware Skin Cancer Epithelial Cell Classification with Scalable Graph Transformerscited by 0
  8. 2026Modeling Whole-Slide Images as Dynamic Tumor Microenvironment Fieldscited by 0