Add pipeline tag and improve model card

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  license: apache-2.0
 
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  ---
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  This model corresponds to the **VQ-Seg training setup on the ACDC dataset**.
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- The model is introduced and evaluated in the following paper:
 
 
 
 
 
 
 
 
 
 
 
 
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- **VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation**
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- arXiv: https://arxiv.org/abs/2601.10124
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- Check: https://github.com/script-Yang/VQ-Seg
 
 
 
 
 
 
 
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  license: apache-2.0
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+ pipeline_tag: image-segmentation
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+ # VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation
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  This model corresponds to the **VQ-Seg training setup on the ACDC dataset**.
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+ 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.
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+ - **Paper:** [VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation](https://arxiv.org/abs/2601.10124)
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+ - **Code:** [GitHub - script-Yang/VQ-Seg](https://github.com/script-Yang/VQ-Seg)
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+ - **Dataset (ACDC-PNG):** [Hugging Face Datasets](https://huggingface.co/datasets/yscript/ACDC-PNG)
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+ ## Key Features
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+ - **Quantized Perturbation Module (QPM):** Replaces dropout with a mechanism that shuffles spatial locations of codebook indices for better regularization.
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+ - **Dual-branch Architecture:** Shares the post-quantization feature space between image reconstruction and segmentation tasks to mitigate information loss.
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+ - **Post-VQ Feature Adapter (PFA):** Incorporates guidance from a foundation model (DINOv2) to supplement high-level semantic information.
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+
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+ ## Citation
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+ If you find this work useful in your research, please consider citing:
 
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+ ```bibtex
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+ @inproceedings{yangvq,
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+ title={VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation},
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+ author={Yang, Sicheng and Xing, Zhaohu and Zhu, Lei},
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+ booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}
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+ }
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+ ```