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---
license: apache-2.0
task_categories:
- image-segmentation
tags:
- medical
- biology
---
# ACDC-PNG Dataset
[Paper](https://arxiv.org/abs/2601.10124) | [Code](https://github.com/script-Yang/VQ-Seg)
This repository contains a convenient PNG-formatted version of the ACDC dataset, primarily intended for semi-supervised medical image segmentation tasks. This version was converted from the files provided in the [SSL4MIS repository](https://github.com/HiLab-git/SSL4MIS/tree/master/data/ACDC).
It is used and introduced in the paper: **VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation**.
### Dataset Structure
The data is organized as follows:
```bash
XXX/
│
├── train-label/ # Labeled training set
│ ├── image/ # Input images (.png)
│ └── mask/ # Corresponding segmentation masks (.png)
│
├── train-unlabel/ # Unlabeled training set
│ └── image/ # Images without ground truth masks
│ └── mask/
│
├── val/ # Validation set
│ ├── image/ # Validation images (.png)
│ └── mask/ # Validation masks (.png)
│
└── test/ # Test set
├── image/ # Test images (.png)
└── mask/ # Test masks (optional)
```
- **train-label**: Paired image–mask samples used for supervised segmentation training.
- **train-unlabel**: Images without ground-truth annotations, utilized for semi-supervised learning.
- **val**: Used to monitor and validate model performance during training.
- **test**: Used for final evaluation and benchmarking.
### Citation
If you use this dataset or the VQ-Seg method in your research, please cite the following:
```bibtex
@inproceedings{yangvq,
title={VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation},
author={Yang, Sicheng and Xing, Zhaohu and Zhu, Lei},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}
}
```
Please also make sure to cite the **original ACDC paper**. |