Hi, thanks for your code! Can you explain to me why in structure_loss function use weights like this? weit = 1 + 5*torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask)

Hi, I wonder to know how can I use the dice_average.m to compute the relationship between dice and the size of polyps. I'm not familiar with matalab , but dice_average.m seems only has a defined function ,I try to call this function in main.m files, but failed. Besides, I also curious about how did you compute the ratio of polyps , does the x axis 0,1,2,3,4,5,6 means the relative size ?

Hello good sir,

i am currently using your architecture as basis for my thesis work and while training the module i wondered about the axial attention module. you utilize a gamma value by setting it this way: self.gamma = nn.Parameter(torch.zeros(1)).

Afterwards you multiply the result of´the axial attention with the gamma value: self.gamma * out and then apply the residual connection of x : out = self.gamma * out + x.

Does this not mean you don't even utilize axial attention in any way by setting the output of axial attention to zero ? and only using the residual output ?

Is this a different version of the code or am i missing something.

I would be very thankful if you could elaborate on this.

Hi, sorry to bother. I'm wondering why I got all the Dice with NaN. I'v check the pathes, but still got NaN in each Dice. cell. The _result.txt is like "(Dataset:CVC-ClinicDB; Model:PraNet) meanDic:NaN;meanIoU:NaN;wFm:NaN;Sm:NaN;meanEm:NaN;MAE:NaN;maxEm:NaN;maxDice:NaN;maxIoU:NaN;meanSen:NaN;maxSen:NaN;meanSpe:NaN;maxSpe:NaN. "

Hi, thanks for ur great work, But I dont see ablation experiments whin ur net, like which part effective the performence more , Do u have done it? I'm very interesting in which modules matters most. thx

Hi.

1. Why there is no premute operation before view in mode h?

# for mode h
projected_query = self.query_conv(x).premute(0, 1, 3, 2).view(*view).permute(0, 2, 1)

2. Why use sigmoid instead of softmax?

Hi, thank you for sharing your code. it is really helpful for me

anyway, i have a question. I tried applying your code to my data. My data label values consist of 0,1 . When applied to this code, the output is 0-255. Can you tell me which part needs to be modified to adjust the output value to 0 and 1? I will wait for your reply. thank you

Sorry to bother you. May I ask about how to calculate the IoU, precision, accuracy, recall , and DSC of every epoch? Or what variables should be used to calculate these indicators？I mean ,which variables are TN, FN, TP,FP.Sorry to trouble you!

Hi I notice that the derivation of axial-attention in the code is as follows：

But in the original version of axial attention it would look like this： I don't fully understand your code, can you explain it for me?I am looking forward to hearing from u.

Regarding the use of self-attention. Before the activation function uses sigmoid, do you need to reduce d**-0.5, because Transformer uses softmax, and it is scaled before operation.

The output of the partial decoder (https://github.com/AngeLouCN/CaraNet/blob/main/lib/partial_decoder.py#L30) has a linear activation. The output of each of the axial attention modules, which are designed for residual learning, go through a BN-PReLU (https://github.com/AngeLouCN/CaraNet/blob/main/CaraNet.py#L47)

The output (decoder + axial transformer 1, 2, 3, 4) then gets a sigmoid activation to generate class probabilities.

Why modify the original linear output (from the partial decoder) with a nonlinear function that biases positive (PRELU)? Doesn't this mean that you're more likely to saturate the sigmoid by having a large input? Or at the very least result in exploding biases for the partial decoder?

My understanding of residual learning is that it's commonly done with no activation functions prior to the summation to prevent exploding biases (continually adding a positive value to a positive value)

Excuse me, mean Dice is greater than 1 when I set --test_path to './TestDataset/Kvasir' './TestDataset/CVC-ColonDB' and './TestDataset/ETIS-LaribPolypDB'.I don't know why that is.