Comments (4)
I'm assuming that in a multi-class dice loss, you'd calculate the dice loss for each label that is not background and then average them. In this case, there is only one label that is not background, so it's hard-coded for the first label. Even though this sorts of make it not multi-class.
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@HadiZayer Ok, I understand. You had better give a notice on the code.
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Yeah, @HadiZayer has it correct, it was a mistake on my part. Since I only used my UNet for two classes, I must have hard-coded that line during debugging. I'll make the necessary corrections. Thanks for bringing it to my attention!
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I've implemented multi-class DICE loss as of my last push.
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Related Issues (16)
- Which BraTS set was used for training? HOT 2
- Questions about BraTS Masks HOT 1
- Loading BRATS dataset and # of training epochs HOT 2
- kU-NET implementation
- 3D Unet unable to locate
- Inference using UNET + BD-LSTM?
- Hi, just a quick thought: difference comparision to 3d-unet
- No module named dataParser HOT 3
- Missing 'losses' module
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- Question about BDC-LSTM input. HOT 2
- Question about prediction output HOT 1
- how to prepare data for bdclstm.py HOT 7
- about the database HOT 4
- Dice Score HOT 3
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