Comments (4)
Hi,
Please use this function to measure the performance.
def calculate_metric_percase(pred, gt):
pred[pred > 0] = 1
gt[gt > 0] = 1
if pred.sum() > 0 and gt.sum() > 0:
dice = metric.binary.dc(pred, gt)
hd95 = metric.binary.hd95(pred, gt, voxelspacing=[10, 1, 1])
asd = metric.binary.asd(pred, gt, voxelspacing=[10, 1, 1])
return dice, hd95, asd
else:
return 0, 50, 10.
Best,
Xiangde.
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Thanks a lot for your clarification.
By the way, in your work "Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer", you report standard deviation, is it calculated over each image? Basically for each foreground class, aside from taking the average over each image's dice, you also calculate the standard deviation, right?
Many thanks
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Hi, Huimin,
Yes, your understanding is right.
Best,
Xiangde.
from ssl4mis.
测试时我将代码改成你上面发布的,但还是出现“The first supplied array does not contain any binary object”,请问大佬这个该怎么解决呢?感谢你的贡献。
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Related Issues (20)
- worker_init_fn in train_cross_teaching_between_cnn_transformer_2D.py
- About the way you create dataloaders for labeled and unlabeled data
- RGB input
- unsupervised loss weight
- Differences between the URCP paper and code
- Note able to get complete ACDC data HOT 1
- 关于deep-co-traing的问题
- DiceLoss uses 'sum' reduction, but CrossEntropyLoss uses 'mean' reduction
- for HOT 3
- train own 2D pictures HOT 2
- EMA in BN layer
- KeyError: '136' HOT 4
- About h5 files HOT 4
- Does R-Drop need two models? HOT 1
- Pseudo label
- metric HOT 1
- downloading dataset HOT 1
- Abouth testing in ACDC dataset
- A tip for this excellent work
- Does Fixmatch support 3D dataset?
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