mlsanityData to 14 Oct 2026

HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images

arXiv 1812.06499 ↗First posted 16 Dec 2018cs.CVcited by 388

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Abstract (arXiv)

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation and classification enables the quantitative analysis of tens of thousands of nuclei within a whole-slide pathology image, opening up possibilities of further analysis of large-scale nuclear morphometry. However, automated nuclear segmentation and classification is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability such as the tumour cells. Additionally, some of the nuclei are often clustered together. To address these challenges, we present a novel convolutional neural network for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass. These distances are then utilised to separate clustered nuclei, resulting in an accurate segmentation, particularly in areas with overlapping instances. Then for each segmented instance, the network predicts the type of nucleus via a devoted up-sampling branch. We demonstrate state-of-the-art performance compared to other methods on multiple independent multi-tissue histology image datasets. As part of this work, we introduce a new dataset of Haematoxylin & Eosin stained colorectal adenocarcinoma image tiles, containing 24,319 exhaustively annotated nuclei with associated class labels.

Key facts. Cited by 388 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Dec 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 34; 2022: 60; 2023: 86; 2024: 66; 2025: 86; 2026: 56.

388 papers.

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  1. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  2. 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 150
  3. 2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 111
  4. 2023CellViT: Vision Transformers for Precise Cell Segmentation and Classificationcited by 108
  5. 2023Artificial Intelligence for Digital and Computational Pathologycited by 108
  6. 2023UniverSeg: Universal Medical Image Segmentationcited by 91
  7. 2023Virchow: A Million-Slide Digital Pathology Foundation Modelcited by 90
  8. 2021Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classificationcited by 89
  9. 2022Mitosis domain generalization in histopathology images -- The MIDOG challengecited by 67
  10. 2021Hierarchical Graph Representations in Digital Pathologycited by 59
  11. 2023Towards a Visual-Language Foundation Model for Computational Pathologycited by 52
  12. 2021CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Imagescited by 51
38 more
  1. 2023Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learningcited by 47
  2. 2021CoNIC: Colon Nuclei Identification and Counting Challenge 2022cited by 45
  3. 2021Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labelscited by 43
  4. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  5. 2023The Multi-modality Cell Segmentation Challenge: Towards Universal Solutionscited by 39
  6. 2022One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classificationcited by 38
  7. 2022WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinomacited by 36
  8. 2021NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationcited by 34
  9. 2023CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Countingcited by 33
  10. 2021CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentationcited by 32
  11. 2022Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Imagescited by 32
  12. 2021SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancercited by 31
  13. 2022Nuclei instance segmentation and classification in histopathology images with StarDistcited by 31
  14. 2023Diffusion-based Data Augmentation for Nuclei Image Segmentationcited by 31
  15. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  16. 2023OCELOT: Overlapped Cell on Tissue Dataset for Histopathologycited by 29
  17. 2023NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Imagescited by 29
  18. 2023DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasetscited by 28
  19. 2025Evidence-based diagnostic reasoning with multi-agent copilot for human pathologycited by 27
  20. 2023ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysiscited by 25
  21. 2023Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challengecited by 25
  22. 2023HEALNet: Multimodal Fusion for Heterogeneous Biomedical Datacited by 25
  23. 2023SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathologycited by 25
  24. 2021Multi-Class Cell Detection Using Spatial Context Representationcited by 24
  25. 2023Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectivescited by 24
  26. 2024Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentationcited by 24
  27. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 23
  28. 2024Towards Large-Scale Training of Pathology Foundation Modelscited by 23
  29. 2021Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Imagecited by 22
  30. 2024UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Imagescited by 22
  31. 2023A Foundational Multimodal Vision Language AI Assistant for Human Pathologycited by 21
  32. 2025Training state-of-the-art pathology foundation models with orders of magnitude less datacited by 21
  33. 2021HistoCartography: A Toolkit for Graph Analytics in Digital Pathologycited by 20
  34. 2024Tyche: Stochastic In-Context Learning for Medical Image Segmentationcited by 20
  35. 2022InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentationcited by 19
  36. 2023Unleashing the Power of Prompt-driven Nucleus Instance Segmentationcited by 19
  37. 2023NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Modelscited by 18
  38. 2023DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Countingcited by 18

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