hilab-git / aceloss Goto Github PK

How simple run project? I want pass image as params and return contour

Hi, thanks for the repo.

When I try to use the ACELoss, the HD Loss is very high for hundreds epochs (approx. 1e+4) while the DSC is around 0.005 or so. I am wondering if I use this repo right.

What I am trying to do is multi-label segmentation, that inputting B1HW and outputting BCHW (where C is the number of classes). Previously, I used Dice loss for my task and it worked well (approx. 0.95 DSC). Currently, I simply switched from dice loss to 0.8 * dice + 0.2 * ACE.

I am wondering if I am using ACE correctly or not?

dear author
This is a very good job. I added ACE loss loss=torch. mean (CEloss+ACEloss) to the already trained model (DICE: 0.90),

but I found that ACEloss has a scale of 100,000, while CEloss has only a decimal scale.

Do I need to multiply a ratio coefficient before ACElos.

Hi, I use the FastACELoss3DV2 as loss function for liver segmentation in nnUNet. Training about 140 epochs but the global dice for validation data set always below 0.04. Is there any wrong with me in using the loss function?

Hello,

I tried to use AC or ACE losses instead of CE loss for the binary segmentation. Though I have used a certain network for the CE loss many times, the network does not work for AC/ACE losses.

In the meantime, I used a label array whose shape is equal to the prediction. Also, its channels have zero or one values like the code below.

from aceloss import ACLossV2
criterion = ACLossV2(classes=2)
outputs = model(inputs)
masks2 = torch.zeros_like(outputs)
masks2[:, 0, :, :] = (masks == 0).squeeze(1) # shape: [batch size, channel size, width, height]
masks2[:, 1, :, :] = (masks == 1).squeeze(1)
loss = criterion(outputs, masks2)

The loss value is larger than 1e4, and the output for the prediction looks meaningless. Did I miss something? I didn't change any code for the ACELoss class. Thank you.

Hello, I want to test the performance of this loss function on other dataset. Coud you explain the meaning of each input dimention in the demo?

x2 = torch.rand((2, 3, 97, 80))
x3 = torch.rand((2, 4, 112, 97, 80))

Good Jod! I tried to use the 3D ACE loss in my project, focusing on 3D vessel segmentation!
Also great job and thanks for PyMIC Repo!
You can also ensemble 3D ACE loss in PyMIC, just my advice!