README.md

---
pipeline_tag: object-detection
tags:
- histopathology
---
# Model Card for KongNet

This repository contains pretrained weights for KongNet, a deep learning model for nuclei detection and classification.

## Model Details

### 2025 MIDOG Challenge Model

- **Developed by:** Tissue Image Analytics (TIA) Centre, University of Warwick, United Kingdom.
- **Model type:** KongNet-Det 
- **License:**  CC BY-NC-SA 4.0
- **Model Stats:**
  - Params(M): 126M
  - Image Size: 512 x 512
  - Resolution: 0.25 mpp
- **Cell Type:**
  - Mitotic Figure
- **Training Datasets**:
  - [MIDOG++](https://www.nature.com/articles/s41597-023-02327-4)
  - [MITOS_WSI_CMC](https://www.nature.com/articles/s41597-020-00756-z)
  - [MITOS_WSI_CCMCT](https://www.nature.com/articles/s41597-019-0290-4.pdf)
  - [Mitosis Dataset for TCGA Diagnostic Slides](https://zenodo.org/records/14548480)
- **Pretrained weights**:
  - `KongNet_Det_MIDOG_1.pth`
  - `KongNet_Det_MIDOG_3.pth`
  - `KongNet_Det_MIDOG_4.pth`
 
### CoNIC Model

- **Developed by:** Tissue Image Analytics (TIA) Centre, University of Warwick, United Kingdom.
- **Model type:** KongNet 
- **License:** CC BY-NC-SA 4.0
- **Model Stats:**
  - Params(M): 176M
  - Image Size: 256 x 256
  - Resolution: 0.5 mpp
- **Cell Type:**
  - Epithelial Cell
  - Lymphocyte
  - Plasma Cell
  - Neutrophil
  - Eosinophil
  - Connective Tissue Cell
- **Training Datasets**:
  - [CoNIC](https://www.sciencedirect.com/science/article/pii/S1361841523003079)
- **Pretrained weights**:
  - `KongNet_CoNIC_1.pth`
  - `KongNet_CoNIC_2.pth`
  - `KongNet_CoNIC_4.pth`
 
### MONKEY Challenge Model

- **Developed by:** Tissue Image Analytics (TIA) Centre, University of Warwick, United Kingdom.
- **Model type:** KongNet (wide)
- **License:** CC BY-NC-SA 4.0
- **Model Stats:**
  - Params(M): 174M
  - Image Size: 256 x 256
  - Resolution: 0.25 mpp
- **Cell Type:**
  - Overall mononuclear leukocyte
  - Lymphocyte
  - Monocyte
- **Training Datasets**:
  - [MONKEY](https://zenodo.org/records/13794656)
- **Pretrained weights**:
  - `KongNet_MONKEY_1.pth`
  - `KongNet_MONKEY_2.pth`
  - `KongNet_MONKEY_4.pth`

### PanNuke Model

- **Developed by:** Tissue Image Analytics (TIA) Centre, University of Warwick, United Kingdom.
- **Model type:** KongNet
- **License:**  CC BY-NC-SA 4.0
- **Model Stats:**
  - Params(M): 176M
  - Image Size: 256 x 256
  - Resolution: 0.25 mpp
- **Cell Type:**
  - Overall
  - Neoplastic Cell
  - Non-Neoplatic Epithelial Cell
  - Inflammatory Cell
  - Dead Cell
  - Connective Cell
- **Training Datasets**:
  - [PanNuke](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke)
- **Pretrained weights**:
  - `KongNet_PanNuke_1.pth`
  - `KongNet_PanNuke_2.pth`
  - `KongNet_PanNuke_3.pth`

### PUMA Challenge Model (Track 1)

- **Developed by:** Tissue Image Analytics (TIA) Centre, University of Warwick, United Kingdom.
- **Model type:** KongNet
- **License:** CC BY-NC-SA 4.0
- **Model Stats:**
  - Params(M): 146M
  - Image Size: 256 x 256
  - Resolution: 0.25 mpp
- **Cell Type:**
  - Tumour Cell
  - Lymphocyte
  - Other Cell
- **Training Datasets**:
  - [PUMA](https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giaf011/8024182?login=true)
- **Pretrained weights**:
  - `KongNet_PUMA_T1_3.pth`
  - `KongNet_PUMA_T1_4.pth`
  - `KongNet_PUMA_T1_5.pth`

### PUMA Challenge Model (Track 2)

- **Developed by:** Tissue Image Analytics (TIA) Centre, University of Warwick, United Kingdom.
- **Model type:** KongNet
- **License:** CC BY-NC-SA 4.0
- **Model Stats:**
  - Params(M): 216M
  - Image Size: 256 x 256
  - Resolution: 0.25 mpp
- **Cell Type:**
  - Tumour Cell
  - Lymphocyte
  - Plasma Cell
  - Histiocyte
  - Melanophage
  - Neutrophil
  - Stroma Cell
  - Epithelial Cell
  - Endothelial Cell
  - Apoptotic Cell
- **Training Datasets**:
  - [PUMA](https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giaf011/8024182?login=true)
- **Pretrained weights**:
  - `KongNet_PUMA_T2_3.pth`
  - `KongNet_PUMA_T2_4.pth`
  - `KongNet_PUMA_T2_5.pth`

### Model Sources

<!-- Provide the basic links for the model. -->

- **Inference Code (Github):** [KongNet_Inference_Main](https://github.com/Jiaqi-Lv/KongNet_Inference_Main)
- **Paper:** [KongNet: A Multi-headed Deep Learning Model for Accurate Detection and Classification of Nuclei in Histopathology Images](https://arxiv.org/abs/2510.23559)

## Citation

If you use this model, please cite:

**BibTeX:**
```
@misc{
  lv2025kongnetmultiheadeddeeplearning,
  title={KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images}, 
  author={Jiaqi Lv and Esha Sadia Nasir and Kesi Xu and Mostafa Jahanifar and Brinder Singh Chohan and Behnaz Elhaminia and Shan E Ahmed Raza},
  year={2025},
  eprint={2510.23559},
  archivePrefix={arXiv},
  primaryClass={eess.IV},
  url={https://arxiv.org/abs/2510.23559}, 
}
```