# CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

arXiv: https://arxiv.org/abs/2303.06274 · HTML: https://mlsanity.com/paper/2303.06274/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 80 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 30 of 45)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Hybrid Task Cascade for Instance Segmentation](https://mlsanity.com/paper/1901.07518/) (2019)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest](https://mlsanity.com/paper/1603.00275/) (2016)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Mish: A Self Regularized Non-Monotonic Activation Function](https://mlsanity.com/paper/1908.08681/) (2019)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary](https://mlsanity.com/paper/2006.13932/) (2020)

## Built on by (most cited first, 30 of 33)

- [The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions](https://mlsanity.com/paper/2308.05864/) (2023)
- [NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images](https://mlsanity.com/paper/2308.01760/) (2023)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model](https://mlsanity.com/paper/2507.17303/) (2025)
- [PathDiff: Histopathology Image Synthesis with Unpaired Text and Mask Conditions](https://mlsanity.com/paper/2506.23440/) (2025)
- [DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions](https://mlsanity.com/paper/2404.14956/) (2024)
- [Tissue Concepts: supervised foundation models in computational pathology](https://mlsanity.com/paper/2409.03519/) (2024)
- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [CAMP: Continuous and Adaptive Learning Model in Pathology](https://mlsanity.com/paper/2407.09030/) (2024)
- [Benchmarking Domain Generalization Algorithms in Computational Pathology](https://mlsanity.com/paper/2409.17063/) (2024)
- [RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization](https://mlsanity.com/paper/2505.05073/) (2025)
- [An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer](https://mlsanity.com/paper/2311.06185/) (2023)
- [Unlocking adaptive digital pathology through dynamic feature learning](https://mlsanity.com/paper/2412.20430/) (2024)
- [A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Images](https://mlsanity.com/paper/2502.19217/) (2025)
- [PhenoBench: A Comprehensive Benchmark for Cell Phenotyping](https://mlsanity.com/paper/2507.03532/) (2025)
- [Tissue Aware Nuclei Detection and Classification Model for Histopathology Images](https://mlsanity.com/paper/2511.13615/) (2025)
- [Nuclear Pleomorphism in Canine Cutaneous Mast Cell Tumors: Comparison of Reproducibility and Prognostic Relevance between Estimates, Manual Morphometry and Algorithmic Morphometry](https://mlsanity.com/paper/2309.15031/) (2023)
- [A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention](https://mlsanity.com/paper/2407.18673/) (2024)
- [Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis](https://mlsanity.com/paper/2412.09521/) (2024)
- [Patherea: Cell Detection and Classification for the 2020s](https://mlsanity.com/paper/2412.16425/) (2024)
- [HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification](https://mlsanity.com/paper/2502.08754/) (2025)
- [CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and Classification](https://mlsanity.com/paper/2504.00784/) (2025)
- [A Novel Approach to Linking Histology Images with DNA Methylation](https://mlsanity.com/paper/2504.05403/) (2025)
- [A Comparison of Deep Learning Methods for Cell Detection in Digital Cytology](https://mlsanity.com/paper/2504.06957/) (2025)
- [Med-2D SegNet: A Light Weight Deep Neural Network for Medical 2D Image Segmentation](https://mlsanity.com/paper/2504.14715/) (2025)
- [PanopMamba: Vision State Space Modeling for Nuclei Panoptic Segmentation](https://mlsanity.com/paper/2601.16631/) (2026)
- [Learning Class Difficulty via Dynamic Focal Attention for Histopathology Segmentation](https://mlsanity.com/paper/2604.13479/) (2026)
- [Dino-NestedUNet: Unlocking Foundation Vision Encoders for Pathology Tumor Bulk Segmentation via Dense Decoding](https://mlsanity.com/paper/2605.00894/) (2026)
- [NucEval: A Robust Evaluation Framework for Nuclear Instance Segmentation](https://mlsanity.com/paper/2605.03144/) (2026)
- [Deep Reprogramming Distillation for Medical Foundation Models](https://mlsanity.com/paper/2605.04447/) (2026)

## Abstract (arXiv)

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.