# CoNIC: Colon Nuclei Identification and Counting Challenge 2022

arXiv: https://arxiv.org/abs/2111.14485 · HTML: https://mlsanity.com/paper/2111.14485/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest](https://mlsanity.com/paper/1603.00275/) (2016)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification](https://mlsanity.com/paper/2108.11195/) (2021)
- [MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images](https://mlsanity.com/paper/1806.01963/) (2018)
- [Quantifying Explainers of Graph Neural Networks in Computational Pathology](https://mlsanity.com/paper/2011.12646/) (2020)
- [Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology](https://mlsanity.com/paper/1712.06657/) (2017)
- [ALBRT: Cellular Composition Prediction in Routine Histology Images](https://mlsanity.com/paper/2108.08306/) (2021)
- [Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology](https://mlsanity.com/paper/2106.08153/) (2021)

## Built on by (most cited first, 30 of 45)

- [CellViT: Vision Transformers for Precise Cell Segmentation and Classification](https://mlsanity.com/paper/2306.15350/) (2023)
- [One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification](https://mlsanity.com/paper/2203.00077/) (2022)
- [CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting](https://mlsanity.com/paper/2303.06274/) (2023)
- [Nuclei instance segmentation and classification in histopathology images with StarDist](https://mlsanity.com/paper/2203.02284/) (2022)
- [Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2411.12814/) (2024)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation](https://mlsanity.com/paper/2305.00678/) (2023)
- [BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels](https://mlsanity.com/paper/2401.07437/) (2024)
- [SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts](https://mlsanity.com/paper/2212.13780/) (2022)
- [Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning](https://mlsanity.com/paper/2303.12095/) (2023)
- [Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images](https://mlsanity.com/paper/2309.07394/) (2023)
- [StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images](https://mlsanity.com/paper/2403.09302/) (2024)
- [Panoptic segmentation with highly imbalanced semantic labels](https://mlsanity.com/paper/2203.11692/) (2022)
- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)
- [Nuclear Segmentation and Classification: On Color & Compression Generalization](https://mlsanity.com/paper/2301.03418/) (2023)
- [CAMP: Continuous and Adaptive Learning Model in Pathology](https://mlsanity.com/paper/2407.09030/) (2024)
- [ConvNeXt-backbone HoVerNet for nuclei segmentation and classification](https://mlsanity.com/paper/2202.13560/) (2022)
- [Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting](https://mlsanity.com/paper/2203.00157/) (2022)
- [Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation](https://mlsanity.com/paper/2402.16280/) (2024)
- [ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network](https://mlsanity.com/paper/2202.13960/) (2022)
- [Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning](https://mlsanity.com/paper/2308.04578/) (2023)
- [RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization](https://mlsanity.com/paper/2505.05073/) (2025)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge](https://mlsanity.com/paper/2202.13588/) (2022)
- [Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector](https://mlsanity.com/paper/2203.01321/) (2022)
- [Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification](https://mlsanity.com/paper/2203.01940/) (2022)
- [MGTUNet: An new UNet for colon nuclei instance segmentation and quantification](https://mlsanity.com/paper/2210.10981/) (2022)
- [More From Less: Self-Supervised Knowledge Distillation for Routine Histopathology Data](https://mlsanity.com/paper/2303.10656/) (2023)
- [Semi-supervised Cell Recognition under Point Supervision](https://mlsanity.com/paper/2306.08240/) (2023)
- [Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures](https://mlsanity.com/paper/2308.10166/) (2023)

## Abstract (arXiv)

Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath). However, automatic recognition of different nuclei is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability. To help drive forward research and innovation for automatic nuclei recognition in CPath, we organise the Colon Nuclei Identification and Counting (CoNIC) Challenge. The challenge encourages researchers to develop algorithms that perform segmentation, classification and counting of nuclei within the current largest known publicly available nuclei-level dataset in CPath, containing around half a million labelled nuclei. Therefore, the CoNIC challenge utilises over 10 times the number of nuclei as the previous largest challenge dataset for nuclei recognition. It is important for algorithms to be robust to input variation if we wish to deploy them in a clinical setting. Therefore, as part of this challenge we will also test the sensitivity of each submitted algorithm to certain input variations.