Comments (1)
Such a two-stream sampling strategy is only used for semi-supervised settings (a batch contains labeled and unlabeled data at the same time). You don't have any unlabeled data in your scenario, so just using the original sampling strategy of pytorch is fine.
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Related Issues (16)
- Did you crop raw samples on the z axis? HOT 1
- 请问可以提供预处理好的Pancreas数据吗? HOT 1
- Asking for the CT-82 dataset HOT 1
- Pancreas dataset preprocessing success,but the DICE score fluctuated around 0.2 to 0.1 during training. HOT 4
- 关于Multi-scale MC-Net+和sharpening函数问题请教 HOT 1
- About the parameter quantity in the MC-Net+ HOT 2
- Number of classes issue HOT 1
- Labels for unlabeled data HOT 3
- 训练标签问题 HOT 1
- train_mcnet_2d.py HOT 3
- Loss function mistake HOT 1
- About the consistency_weight HOT 1
- Data preparation for ACDC, LA and Pancreas HOT 6
- Type error when training VNet with 10% LA dataset HOT 3
- Unmatched tensors HOT 12
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