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Fix broken link in swin-unetr notebook (#1862)
Fixes # . ### Description A few sentences describing the changes proposed in this pull request. ### Checks <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [ ] Avoid including large-size files in the PR. - [ ] Clean up long text outputs from code cells in the notebook. - [ ] For security purposes, please check the contents and remove any sensitive info such as user names and private key. - [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use relative paths for tutorial repo files (3) put figure and graphs in the `./figure` folder - [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>` Signed-off-by: YunLiu <[email protected]>
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3d_segmentation/swin_unetr_btcv_segmentation_3d.ipynb

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"\n",
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"For this tutorial, the dataset needs to be downloaded from: https://www.synapse.org/#!Synapse:syn3193805/wiki/217752. More details are provided in the \"Download dataset\" section below.\n",
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"In addition, the json file for data splits needs to be downloaded from this [link](https://drive.google.com/file/d/1qcGh41p-rI3H_sQ0JwOAhNiQSXriQqGi/view?usp=sharing). Once downloaded, place the json file in the same folder as the dataset. \n",
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"In addition, the json file for data splits needs to be downloaded from this [link](https://developer.download.nvidia.com/assets/Clara/monai/tutorials/swin_unetr_btcv_dataset_0.json). Once downloaded, place the json file in the same folder as the dataset. \n",
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"For BTCV dataset, under Institutional Review Board (IRB) supervision, 50 abdomen CT scans of were randomly selected from a combination of an ongoing colorectal cancer chemotherapy trial, and a retrospective ventral hernia study. The 50 scans were captured during portal venous contrast phase with variable volume sizes (512 x 512 x 85 - 512 x 512 x 198) and field of views (approx. 280 x 280 x 280 mm3 - 500 x 500 x 650 mm3). The in-plane resolution varies from 0.54 x 0.54 mm2 to 0.98 x 0.98 mm2, while the slice thickness ranges from 2.5 mm to 5.0 mm. \n",
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"We use weights from self-supervised pre-training of Swin UNETR encoder (3D Swin Tranformer) on a cohort of 5050 CT scans from publicly available datasets. The encoder is pre-trained using reconstructin, rotation prediction and contrastive learning pre-text tasks as shown below. For more details, please refer to [1] (CVPR paper) and see this [repository](https://github.com/Project-MONAI/research-contributions/tree/main/SwinUNETR/Pretrain). \n",
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"![image](https://lh3.googleusercontent.com/pw/AM-JKLVLgduGZ9naCSasWg09U665NBdd3UD4eLTy15wJiwbmKLS_p5WSZ2MBcRePEJO2tv9X3TkC52MsbnomuPy5JT3vSVeCji1MOEuAzcsxily88TdbHuAt6PzccefwKupbXyOCumK5hzz5Ul38kZnlEQ84=w397-h410-no?authuser=2)\n",
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"Please download the pre-trained weights from this [link](https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/model_swinvit.pt) and place it in the root directory of this tutorial. \n",
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"If training from scratch is desired, please skip the step for initializing from pre-trained weights. "

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