| | --- |
| | 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} |
| | } |
| | ``` |
