# ConvNeXt-backbone HoVerNet for nuclei segmentation and classification

arXiv: https://arxiv.org/abs/2202.13560 · HTML: https://mlsanity.com/paper/2202.13560/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification](https://mlsanity.com/paper/2108.11195/) (2021)
- [CoNIC: Colon Nuclei Identification and Counting Challenge 2022](https://mlsanity.com/paper/2111.14485/) (2021)
- [CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation](https://mlsanity.com/paper/2102.06867/) (2021)
- [Mask-RCNN and U-net Ensembled for Nuclei Segmentation](https://mlsanity.com/paper/1901.10170/) (2019)

## Built on by (most cited first, 3 of 3)

- [Nuclear Segmentation and Classification: On Color & Compression Generalization](https://mlsanity.com/paper/2301.03418/) (2023)
- [Early Churn Prediction from Large Scale User-Product Interaction Time Series](https://mlsanity.com/paper/2309.14390/) (2023)
- [An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture](https://mlsanity.com/paper/2508.11532/) (2025)

## Abstract (arXiv)

This manuscript gives a brief description of the algorithm used to participate in CoNIC Challenge 2022. After the baseline was made available, we follow the method in it and replace the ResNet baseline with ConvNeXt one. Moreover, we propose to first convert RGB space to Haematoxylin-Eosin-DAB(HED) space, then use Haematoxylin composition of origin image to smooth semantic one hot label. Afterwards, nuclei distribution of train and valid set are explored to select the best fold split for training model for final test phase submission. Results on validation set shows that even with channel of each stage smaller in number, HoVerNet with ConvNeXt-tiny backbone still improves the mPQ+ by 0.04 and multi r2 by 0.0144