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license: apache-2.0 |
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--- |
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# [ 🎯 NeurIPS 2025 ] 3D-RAD 🩻: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks |
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<div align="center"> |
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<a href="https://github.com/Tang-xiaoxiao/3D-RAD/stargazers"> |
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<img src="https://img.shields.io/github/stars/Tang-xiaoxiao/3D-RAD?style=social" /> |
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</a> |
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<a href="https://arxiv.org/abs/2506.11147"> |
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<img src="https://img.shields.io/badge/arXiv-Paper-b31b1b.svg?logo=arxiv" /> |
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</a> |
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<a href="https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity"> |
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<img src="https://img.shields.io/badge/Maintained%3F-yes-green.svg" /> |
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</a> |
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</div> |
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## 📢 News |
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<summary><strong>What's New in This Update 🚀</strong></summary> |
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- **2025.10.23**: 🔥 Updated **the latest version** of the paper! |
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- **2025.09.19**: 🔥 Paper accepted to **NeurIPS 2025**! 🎯 |
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- **2025.05.16**: 🔥 Set up the repository and committed the dataset! |
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## 🔍 Overview |
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💡 In this repository, we present the dataset for **["3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks"](https://arxiv.org/pdf/2506.11147)**. |
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The code for our project is available on GitHub: [Tang-xiaoxiao/3D-RAD](https://github.com/Tang-xiaoxiao/3D-RAD) |
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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). |
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## 📊 3D-RAD Dataset |
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In the `3DRAD` directory, there are QA data without 3D images. |
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You can find the full dataset with 3D images (For efficient model input, the original CT images were preprocessed and converted into .npy format.) in [3D-RAD_Dataset](https://huggingface.co/datasets/Tang-xiaoxiao/3D-RAD). |
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### 📁 Images/ |
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This folder contains preprocessed 3D CT volumes in `.npy` format. |
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Each file is structured to facilitate direct input into vision-language models. |
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- Purpose: Standardized model input across all tasks. |
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### 📁 train/ and 📁 test/ |
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These folders contain the question-answer (QA) pairs categorized by task. |
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Each file corresponds to a specific QA task such as anomaly detection, measurement, or temporal reasoning. |
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- `train/`: QA pairs for model training |
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- `test/`: QA pairs for model evaluation |
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### Fields: |
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- `VolumeName`: File name of the associated CT volume (matches the file in `Images/`) |
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- `Question`: The natural language question |
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- `Answer`: The ground truth answer |
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- `QuestionType`: Either `open` or `closed` |
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- `AnswerChoice`: Correct option (A/B/C/D) for closed questions |
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- `Choice A`–`Choice D`: Candidate options for closed questions |
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## 🤖 M3D-RAD Model |
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To assess the utility of 3D-RAD, we **finetuned two M3D model variants** with different parameter scales, thereby constructing the M3D-RAD models. You can find our finetuned model in [M3D-RAD_Models](https://huggingface.co/Tang-xiaoxiao/M3D-RAD). |
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## 📈 Evaluation |
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### Zero-Shot Evaluation. |
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We conducted **zero-shot evaluation** of several stateof-the-art 3D medical vision-language models on our benchmark to assess their generalization capabilities. |
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In the `RadFM` and `M3D` directory, there are code for evaluating RadFM and M3D models on our 3D-RAD benchmark. Note that, the base code is [RadFM](https://github.com/chaoyi-wu/RadFM), and the base code is [M3D](https://github.com/BAAI-DCAI/M3D). To run our evaluation, you should first satisfy the requirements and download the models according to the base code of these models. |
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Compare to the base code, we make the following modifications: In the `RadFM` directory, we add a new Dataset in `RadFM/src/Dataset/dataset/rad_dataset.py` and modify the Dataset to test in `RadFM/src/Dataset/multi_dataset_test.py`. Then we add a new python file to evaluate our benchmark in `RadFM/src/eval_3DRAD.py`. In the `M3D` directory, we add a new Dataset in `M3D/Bench/dataset/multi_dataset.py` and add a new python file to evaluate our benchmark in `M3D/Bench/eval/eval_3DRAD.py`. |
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You can evaluate RadFM on our 3D-RAD benchmark by running: |
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```python |
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cd 3D-RAD/RadFM/src |
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python eval_3DRAD.py \ |
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--file_path={your test file_path} \ |
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--output_path={your saved output_path} |
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``` |
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You can evaluate M3D on our 3D-RAD benchmark by running: |
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```python |
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cd 3D-RAD/M3D |
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python Bench/eval/eval_3DRAD.py \ |
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--model_name_or_path={your model_name} \ |
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--vqa_data_test_path={your test file_path} \ |
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--output_dir={your saved output_dir} |
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``` |
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### Scaling with Varying Training Set Sizes. |
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To further investigate the impact of dataset scale on model performance, we randomly **sampled 1%, 10% and 100%** of the training data per task and fine-tuned M3D accordingly. |
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## 📁 Data Source |
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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. |
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## 🔗 Model Links |
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| Model | Paper | |
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| ----- | ------------------------------------------------------------ | |
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| [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 | |
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| [M3D](https://github.com/BAAI-DCAI/M3D) | M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models | |
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| OmniV(not open) | OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding | |
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