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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+# 3D-RAD
+The official Dataset for the paper "3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks".
+
+In our project, we collect a large-scale dataset designed to advance 3D Med-VQA using radiology CT scans, 3D-RAD, encompasses six diverse VQA tasks: anomaly detection (task 1), image observation (task 2), medical computation (task 3), existence detection (task 4), static temporal diagnosis (task 5), and longitudinal temporal diagnosis (task 6). 
+
+![Main Figure](https://github.com/Tang-xiaoxiao/M3D-RAD/blob/main/Figures/main.png?raw=true)
+
+
+## 📁 Images/
+This folder contains preprocessed 3D CT volumes in `.npy` format.  
+Each file is structured to facilitate direct input into vision-language models.  
+- Purpose: Standardized model input across all tasks.
+
+## 📁 train/ and 📁 test/
+These folders contain the question-answer (QA) pairs categorized by task.  
+Each file corresponds to a specific QA task such as anomaly detection, measurement, or temporal reasoning.
+
+- `train/`: QA pairs for model training  
+- `test/`: QA pairs for model evaluation  
+
+# Fields:
+- `VolumeName`: File name of the associated CT volume (matches the file in `Images/`)  
+- `Question`: The natural language question  
+- `Answer`: The ground truth answer  
+- `QuestionType`: Either `open` or `closed`  
+- `AnswerChoice`: Correct option (A/B/C/D) for closed questions  
+- `Choice A`–`Choice D`: Candidate options for closed questions  
+
+## Code
+You can find our code in [M3D-RAD_Code](https://github.com/Tang-xiaoxiao/M3D-RAD).
+
+## M3D-RAD Model
+You can find our model in [M3D-RAD_Models](https://huggingface.co/Tang-xiaoxiao/M3D-RAD).
+
+## Data Source
+The original CT scans in our dataset are derived from [CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE), which is released under a CC-BY-NC-SA license. We fully comply with the license terms by using the data for non-commercial academic research, providing proper attribution.
+
+## Model Links
+
+| Model | Paper                                                        |
+| ----- | ------------------------------------------------------------ |
+| [RadFM](https://github.com/chaoyi-wu/RadFM) | Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data | https://github.com/chaoyi-wu/RadFM |
+| [M3D](https://github.com/BAAI-DCAI/M3D)   | M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models |
+| OmniV(not open) | OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding |