DVCTNet

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[MICCAI 2025] Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training

DVCTNet 최신버젼 다운로드

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# DVCTNet
[MICCAI 2025] Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training

by Tao Luo \*, [Han Wu](https://hanwu.website/)\*, Tong Yang, [Dinggang Shen](https://idea.bme.shanghaitech.edu.cn/), and [Zhiming Cui](https://shanghaitech-impact.github.io/)<sup>+</sup>

[[Paper](https://arxiv.org/abs/2508.20813)]   [[Project Page](https://shanghaitech-impact.github.io/DVCTNet/)]

This repository contains the code and dataset for our paper "Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training" in MICCAI 2025.

## Updates
- [09/2025] Dataset released!
- [09/2025] Source code released!
- [06/2025] Our paper is accepted by MICCAI 2025!

## Getting Started

### Requirements
- Python 3.9+
- PyTorch 2.1+
- CUDA runtime 11.8 (recommended)
- mmdet 3.3.0
- mmcv 2.1.0
   
Run the following command to install the required packages with conda:

```
git clone https://github.com/ShanghaiTech-IMPACT/DVCTNet.git
conda create -f environment.yml
```

Install all the required packages by pip(unrecommended):
```
git clone https://github.com/ShanghaiTech-IMPACT/DVCTNet.git
pip install -r requirements.txt
```

If you have any questions about the installation process, please refer to the [mmdetection documentation](https://mmdetection.readthedocs.io/en/v3.3.0/get_started.html).

## Training and Testing
Before training and testing, the environment variables PYTHONPATH should be appended:

```
export PYTHONPATH=$PYTHONPATH:./mmdet_custom
```
To train the model, run the following command:

```
python tools/train.py \
    configs/models/dvctnet_dinov2_base_fpn_50_epoch.py
```
or you can use the shell script `train.sh` in scripts folder to train the model.
```
bash scripts/train.sh
```

To test the model, run the following command:

```
python tools/test.py \
    configs/models/dvctnet_dinov2_base_fpn_50_epoch.py \
    <checkpoint_path> \
```

To learn more about the configuration files and training and testing process, please refer to the [mmdetection documentation](https://mmdetection.readthedocs.io/en/v3.3.0/).

## Dataset

★ Our dataset is available for reserach purpose only. To apply for DVCT dataset, please refer to the dataset website of IMPACT Lab and fill out the [form](https://shanghaitech-impact.github.io/assets/dataset_application.pdf) and send the signed e-copy to Tao Luo (email: luotao2024@shanghaitech.edu.cn) and Dr. Zhiming Cui (email: cuizhm@shanghaitech.edu.cn) as well as CC your advisor as mentioned in Sec.5 of the form. We will send you the dataset link and password when recieving the data registration form.

## Citation

If you find this code or dataset useful, please cite our paper:

```bibtex
@InProceedings{LuoTao_Adapting_MICCAI2025,
        author = { Luo, Tao and Wu, Han and Yang, Tong and Shen, Dinggang and Cui, Zhiming},
        title = { { Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
        year = {2025},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15975},
        month = {September},
        page = {44 -- 53}
}
```

## Acknowledgements
- This code is built on [mmdetection](https://github.com/open-mmlab/mmdetection) , [ViTAdapter](https://github.com/czczup/ViT-Adapter), [Timm](https://github.com/rwightman/pytorch-image-models), and [Dinov2](https://github.com/facebookresearch/dinov2).

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