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This work presents **VoCo**, a new method for Large-Scale 3D Medical Image Pre-training. We release a new benchmark, including **160K** volumes (**42M** slices) for pre-training, **31M~1.2B** params of pre-trained models, various pre-training recipes, and **50+** downstream tasks implementation.
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Linshan Wu, Jiaxin Zhuang, and <a href="https://scholar.google.com/citations?hl=en&user=Z_t5DjwAAAAJ">**Hao Chen**</a>. [**"Large-Scale 3D Medical Image Pre-training with Geometric Context Priors"**](https://arxiv.org/abs/2410.09890).
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Paper link: https://arxiv.org/abs/2410.09890
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This work presents **VoCo**, a new method for Large-Scale 3D Medical Image Pre-training. We release a new benchmark, including **160K** volumes (**42M** slices) for pre-training, **31M~1.2B** params of pre-trained models, various pre-training recipes, and **50+** downstream tasks implementation.
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Linshan Wu, Jiaxin Zhuang, and <a href="https://scholar.google.com/citations?hl=en&user=Z_t5DjwAAAAJ">**Hao Chen**</a>. [**"Large-Scale 3D Medical Image Pre-training with Geometric Context Priors"**](https://arxiv.org/abs/2410.09890). TPAMI 2025.
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Paper link: https://arxiv.org/abs/2410.09890
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