Update README.md

README.md

@@ -1,27 +1,55 @@
 ---
 license: apache-2.0
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-# 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".
+# [ 🎯 NeurIPS 2025 ] 3D-RAD 🩻: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks
+<div align="center">
+  <a href="https://github.com/Tang-xiaoxiao/3D-RAD/stargazers">
+    <img src="https://img.shields.io/github/stars/Tang-xiaoxiao/3D-RAD?style=social" />
+  </a>
+  <a href="https://arxiv.org/abs/2506.11147">
+    <img src="https://img.shields.io/badge/arXiv-Paper-b31b1b.svg?logo=arxiv" />
+  </a>
+  <a href="https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity">
+    <img src="https://img.shields.io/badge/Maintained%3F-yes-green.svg" />
+  </a>
+</div>
 
-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). 
+## 📢 News
 
-![Main Figure](https://github.com/Tang-xiaoxiao/M3D-RAD/blob/main/Figures/main.png?raw=true)
+<summary><strong>What's New in This Update 🚀</strong></summary>
 
+- **2025.10.23**: 🔥 Updated **the latest version** of the paper!
+- **2025.09.19**: 🔥 Paper accepted to **NeurIPS 2025**! 🎯
+- **2025.05.16**: 🔥 Set up the repository and committed the dataset!
 
-## 📁 Images/
+## 🔍 Overview
+💡 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)**.
+
+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). 
+
+![overview](https://github.com/Tang-xiaoxiao/3D-RAD/blob/main/Figures/overview.png?raw=true)
+![main](https://github.com/Tang-xiaoxiao/3D-RAD/blob/main/Figures/main.png?raw=true)
+
+## 📊 3D-RAD Dataset
+In the `3DRAD` directory, there are QA data without 3D images.
+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).
+
+![distribution](https://github.com/Tang-xiaoxiao/3D-RAD/blob/main/Figures/distribution.png?raw=true)
+![construction](https://github.com/Tang-xiaoxiao/3D-RAD/blob/main/Figures/Construction.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/
+### 📁 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:
+### Fields:
 - `VolumeName`: File name of the associated CT volume (matches the file in `Images/`)  
 - `Question`: The natural language question  
 - `Answer`: The ground truth answer  
@@ -29,16 +57,50 @@ Each file corresponds to a specific QA task such as anomaly detection, measureme
 - `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
+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).
+
+![finetuned](https://github.com/Tang-xiaoxiao/3D-RAD/blob/main/Figures/finetuned.png?raw=true)
+
+## 📈 Evaluation
+
+### Zero-Shot Evaluation.
+We conducted **zero-shot evaluation** of several stateof-the-art 3D medical vision-language models on our benchmark to assess their generalization capabilities.
+
+![zeroshot](https://github.com/Tang-xiaoxiao/3D-RAD/blob/main/Figures/zeroshot.png?raw=true)
+
+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.
+
+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`.
+
+You can evaluate RadFM on our 3D-RAD benchmark by running:
+
+```python
+cd 3D-RAD/RadFM/src
+python eval_3DRAD.py \
+--file_path={your test file_path} \
+--output_path={your saved output_path}
+```
+
+You can evaluate M3D on our 3D-RAD benchmark by running:
+
+```python
+cd 3D-RAD/M3D
+python Bench/eval/eval_3DRAD.py \
+--model_name_or_path={your model_name} \
+--vqa_data_test_path={your test file_path} \
+--output_dir={your saved output_dir}
+```
+
+### Scaling with Varying Training Set Sizes.
+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. 
 
-## M3D-RAD Model
-You can find our model in [M3D-RAD_Models](https://huggingface.co/Tang-xiaoxiao/M3D-RAD).
+![varysizes](https://github.com/Tang-xiaoxiao/3D-RAD/blob/main/Figures/varysizes.png?raw=true)
 
-## Data Source
+## 📁 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 Links
 
 | Model | Paper                                                        |
 | ----- | ------------------------------------------------------------ |