Comments (6)
Juny-Chen
commented on August 26, 2024
i also meet this problem
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Chi20
commented on August 26, 2024
this problem too
---hope someone could solve this.
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Chi20
commented on August 26, 2024
find a solution, who meet with this problem can add arguments not mentioned as below：
referring to:https://theaisummer.com/medical-image-deep-learning/#implementation-details Putting it all together/code part.
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Chi20
commented on August 26, 2024
Then you will meet with this problem:
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Grairain
commented on August 26, 2024
Then you will meet with this problem:
do you know how to solve this???
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yeshunlong
commented on August 26, 2024
Then you will meet with this problem:
do you know how to solve this???
You can set loadData to False and run the train_with_trainer_class.py, then the -samples-.txt will be generated
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Related Issues (20)
- Version 1.0
- Something trouble with inference.py HOT 4
- About img_nii shape in method 'load_medical_image' HOT 1
- AttributeError: 'MRIDatasetMRBRAINS2018' object has no attribute 'affine' HOT 2
- Sharing pre-trained model weights.
- RuntimeError: Expected 5-dimensional input for 5-dimensional weight 8 4 3 3, but got 4-dimensional input of size [4, 256, 64, 64] instead HOT 8
- Example command lines for running COVID segmentation example?
- AttributeError: 'Namespace' object has no attribute 'loadData' HOT 1
- About the format of using my own data
- About
- MICCAI_BraTS_2018_Data_Training/brats2018-list-' + mode + '-samples-' + str(samples) + '.txt' HOT 1
- Low validation DSC when training on Brats2018 HOT 4
- Hi, How to get this kind of results using highresnet?
- The OutputTransition of the VNet model exists with one more than Conv3D
- full_volume.shape ==> "None type has no attribute shape" HOT 1
- Data size into the model
- 没看懂数据的结构
- The error message "Expected 5-dimensional input for 5-dimensional weight [8, 3, 3, 3, 3], but got 4-dimensional input of size [4, 384, 128, 48] instead" indicates that there is a mismatch between the expected input dimensions (5-dimensional) and the actual input dimensions provided (4-dimensional). HOT 2
- Data directory structure HOT 1
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