metadata
license: gpl-3.0
pipeline_tag: object-detection
tags:
- open-vocabulary-detection
- cytopathology
- medical-imaging
CytoSPM
Model weights of CytoSPM: Open-Vocabulary Cytopathology Detection with Structured Prompt Bank (BIBM 2026).
- Paper: arXiv:2609.31314
- Code: github.com/CVIU-CSU/CytoSPM
Files
| File | Backbone | Base AP / AP50 / AP75 | Novel AP / AP50 / AP75 | SHA256 |
|---|---|---|---|---|
cytospm_convnext_t.pth |
ConvNeXt-T | 33.54 / 52.20 / 38.16 | 15.00 / 21.00 / 17.10 | bc94215314319f712c731e8f5f24388df29d254cc2029695d32e815efb3aac4d |
This is the model reported in the paper (Table III). The numbers above were measured with the released code on an RTX 3090; the paper values (33.55 / 52.20 / 38.17 and 15.00 / 21.00 / 17.20) were measured on an RTX 4090. Base AP is the COCO AP over the 24 base categories of PentaCyto; novel AP is the zero-shot COCO AP over the 9 held-out categories.
The file contains model weights only (state_dict) and loads with
torch.load(..., weights_only=True).
Usage
git clone https://github.com/CVIU-CSU/CytoSPM.git && cd CytoSPM
CKPT_FILES=cytospm_convnext_t.pth bash tools/fetch_checkpoints.sh # downloads and checks SHA256
python tools/test.py configs/cytospm.py checkpoints/cytospm_convnext_t.pth
python tools/test_novel.py configs/cytospm.py checkpoints/cytospm_convnext_t.pth
See the code repository for installation, data preparation and training. The PentaCyto images and annotations are not included.
Citation
@inproceedings{li2026cytospm,
title = {CytoSPM: Open-Vocabulary Cytopathology Detection with Structured Prompt Bank},
author = {Li, Wenjie and Xu, Zishan and Huang, Jinyang and Nie, Zhengxin and Kan, Shichao and Liang, Yixiong},
booktitle = {IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
year = {2026}
}