| 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} | |
| } | |
| ``` |