---
license: apache-2.0
pipeline_tag: image-segmentation
---

# VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation

This model corresponds to the **VQ-Seg training setup on the ACDC dataset**.

VQ-Seg is the first approach to employ vector quantization (VQ) to discretize the feature space in semi-supervised medical image segmentation. It introduces a controllable Quantized Perturbation Module (QPM) that replaces traditional dropout, enabling effective regularization by shuffling spatial locations of codebook indices.

- **Paper:** [VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation](https://arxiv.org/abs/2601.10124)
- **Code:** [GitHub - script-Yang/VQ-Seg](https://github.com/script-Yang/VQ-Seg)
- **Dataset (ACDC-PNG):** [Hugging Face Datasets](https://huggingface.co/datasets/yscript/ACDC-PNG)

## Key Features

- **Quantized Perturbation Module (QPM):** Replaces dropout with a mechanism that shuffles spatial locations of codebook indices for better regularization.
- **Dual-branch Architecture:** Shares the post-quantization feature space between image reconstruction and segmentation tasks to mitigate information loss.
- **Post-VQ Feature Adapter (PFA):** Incorporates guidance from a foundation model (DINOv2) to supplement high-level semantic information.

## Citation

If you find this work useful in your research, please consider citing:

```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}
}
```