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I am one of the main authors and developers of SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation and here is our official [repository](https://github.com/OSUPCVLab/SegFormer3D). …
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Thank you very much for your research. I have read your paper published on CVPR and I would like to run this code.
I obtained the BraTS dataset, but this is not the same as the input of the code. I…
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Hi,
I'm trying to finetune nnUNet for a brain tumor segmentation task using pretrained weights from Task082_BraTS2020. My data consists of brain MRI images with 4 input modalities, and the labels m…
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Hi everyone,
first of all thank you for providing this tool to the public.
I tried to run the `brats-segment` script from the command line on my Ubuntu 18.04 virtual machine:
`brats-segment -…
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Hi @TaWald,
I would like to check which information was crucial to replicate lesion-wise evaluation which is used for HD-BM paper.
What I have found in the supplement document is the following:
…
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When running the BraTS 2021 notebook (located at PyTorch/Segmentation/nnUNet/notebooks/BraTS21.ipynb) training section, the model is not properly training even though it is going through the steps, as…
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def convert_brats_data(brats_folder, out_folder, bias_correct=True, overwrite=True, no_bias_correction_modalities=("flair",)):
for subject_folder in tqdm(glob.glob(os.path.join("data", "*",…
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**Describe the bug**
MONAI Label server is giving the following error when "brats_mri_segmentation_v0.2.1" is used for brain tumor segmentation.
**RuntimeError: Given groups=1, weight of size [16,…
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Hello!
What an amazing github, I can tell that a lot effort has been put into it.
I would like to train the swin UNETR model https://arxiv.org/pdf/2201.01266.pdf from scratch, with the dataset.…
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Dear,
With great intrest I have read your paper on Swin UNETR for brain tumor segmentation as it seems to solve our exact problem when segmenting small tumors (using context vs having a high resolu…