Update README.md

README.md

@@ -6,4 +6,89 @@ 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}
+}
+```