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README.md
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- **label_192.zip**: Contains 192 volumes with pixel-level annotations (Files need to be suffixed nii.gz).
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- You can directly download it: wget https://huggingface.co/datasets/WuBiao/BHSD/resolve/main/label_192.zip?download=true
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- **unlabel_2000.zip**: Contains 2000 volumes of unannotated reconstructed data.
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## Applications
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This dataset is primarily intended to support the use of deep learning techniques in medical image segmentation tasks, particularly for multi-class segmentation of intracranial hemorrhages. It can be used for supervised and semi-supervised ICH segmentation tasks, and we provide experimental results with state-of-the-art models as reference benchmarks.
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- **label_192.zip**: Contains 192 volumes with pixel-level annotations (Files need to be suffixed nii.gz).
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- You can directly download it: wget https://huggingface.co/datasets/WuBiao/BHSD/resolve/main/label_192.zip?download=true
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- **unlabel_2000.zip**: Contains 2000 volumes of unannotated reconstructed data.
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- You can directly download it: wget https://huggingface.co/datasets/WuBiao/BHSD/resolve/main/unlabel_2000.zip?download=true
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## Applications
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This dataset is primarily intended to support the use of deep learning techniques in medical image segmentation tasks, particularly for multi-class segmentation of intracranial hemorrhages. It can be used for supervised and semi-supervised ICH segmentation tasks, and we provide experimental results with state-of-the-art models as reference benchmarks.
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