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Singpath_CytoText

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Question Answering
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Singpath_CytoText
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---
license: apache-2.0
task_categories:
- question-answering
tags:
- medical
size_categories:
- 10K<n<100K
---


# Dataset for Singpath-VL



- **Paper:** [Singpath-VL](https://arxiv.org/pdf/2602.09523)

## Singpath-CytoText
### Dataset Description

Singpath-CytoText is a cervical cytology image dataset with structured pathological descriptions and annotations. Each image contains a bounding box highlighting anchor cell and provides a **structured description** (e.g., nuclear size, chromatin pattern, nuclear membrane status, etc.), a natural language **caption**, and a final cytological **label** (e.g., ASC-H). This dataset can be used for multimodal learning, vision-language pre-training, and medical image report generation.

### Supported Tasks and Leaderboards

- **Image Classification**: Predict the Bethesda category (e.g., ASC-H, NILM) from a cell image.
- **Vision-Language Modeling**: Generate structured pathological descriptions or reports from images.
- **Cross-Modal Retrieval**: Retrieve similar cell images using natural language queries.

### Languages

All text fields (structured description, caption, label) are in Chinese.

## Dataset Structure

### Data Instances

A typical data point from `Singpath-CytoText.json`:

```json
{
    "image_name": "dEPhZAhdwBFttBzk_2363_130_280_244.jpg",
    "structured description": {
        "核大小": "增大",
        "核染色质": "不均匀，颗粒粗，聚集明显",
        "核数量": "单核",
        "核质比": "增高",
        "核膜": "不规则",
        "核仁": "未见",
        "胞质状态": "胞浆量少，着色浅，无特殊结构",
        "异常病理指征": "细胞核异型性：存在；角化异常：未见，不存在挖空细胞",
        "细胞空间构型": "未见聚集成团"
    },
    "caption": "框中细胞核增大，核染色质不均匀、深染，颗粒较粗且聚集明显；核膜轮廓不规则，核质比增高，为单核结构，未见明显核仁。胞浆量较少，着色较浅，无空泡化或其他特殊结构。细胞呈分散分布，未见聚集成团。。不存在挖空细胞。",
    "label": "ASC-H"
}
```

## CytoCell-Bench

### Dataset Description

CytoCell-Bench is a benchmark dataset for cervical cytology image classification. Each image corresponds to a cell region and provides a classification label according to the Bethesda system (e.g., NILM, ASC-US, LSIL, HSIL). This dataset is intended to serve as a standardized testbed for cytology image classification algorithms.

### Supported Tasks and Leaderboards

- **Image Classification**: Predict the Bethesda category of a cell image. This task can serve as a benchmark in medical image classification.

### Languages

Labels are in English abbreviations (e.g., NILM) following the international Bethesda reporting system.

## Dataset Structure

### Data Instances

A typical data point from `CytoCell-Bench.json`:

```json
{
    "image_name": "AIMS-419_19096.0_44486.0_crop_000.jpg",
    "label": "NILM"
}
```

## 📖 Citation
If you find the dataset useful in your research, please consider citing:
```
@article{qiu2026singpath,
  title={Singpath-VL Technical Report},
  author={Qiu, Zhen and Xiao, Kaiwen and Lu, Zhengwei and Liu, Xiangyu and Zhao, Lei and Zhang, Hao},
  journal={arXiv preprint arXiv:2602.09523},
  year={2026}
}
```