hilab-git / dtc Goto Github PK

Hi authors,
Thanks for your great work!

I'm trying to understand your code and I found it difficult to comprehend the training process. Can you help me pinpoint where the supervised training is happening? can you refer me to the specific lines, please?

Hi,
Since there is no VAL data used in LA, how is the best model selected for testing and reporting dice?
Thanks in advance!

Thank you for the nice work!

About compute_sdf function, the VNet's outputs shape is (batch_size, num_classes, x, y, z), isn't it?

if so, the num_classes is set to 0 at outputs[:labeled_bs, 0, ...].shape (please see

Thanks in advance.

Dear author,
In this repository, you have provided the training code and LA dataset. Could you please provide the CT Pancrease dataset mentioned in your paper?
Hope for your reply.

Hello，
thanks for your nice work.
I have some confusion as I look your code in losses.dice_loss.
i find dice in the end is the sum. when 1 - loss(why not n - loss), the results is a negative number.
I did an experiment as follows.

def dice_loss(score, target):
    target = target.float()
    smooth = 1e-5
    intersect = torch.sum(score * target)
    y_sum = torch.sum(target * target)
    z_sum = torch.sum(score * score)
    loss = (2 * intersect + smooth) / (z_sum + y_sum + smooth)
    loss = 1 - loss
    return loss

a=np.array([[[[[0, 1], [1, 0]],[[0, 1],[1, 0]]]], [[[[0, 1], [1, 0]],[[0, 1],[1, 0]]]]])
b=np.array([[[[[0, 1], [1, 1]],[[0, 1],[1, 1]]]], [[[[0, 1], [1, 1]],[[0, 1],[1, 1]]]]])

a = torch.autograd.Variable(torch.from_numpy(a))
b = torch.autograd.Variable(torch.from_numpy(b))

t = dice_loss(a[:2, 0, :, :, :], b[:2] == 1)
print(t)

tensor(-0.6000)

Thats so weird. Did I misunderstand something in any process. Please tell me.
Best.

Hi,
Thanks for your great work!

Can you tell me how to map the output of LSF task to the space of segmentation output for multi-classes. In your work, "dis_to_mask = torch.sigmoid(-1500 * outputs_tanh)" was used to transfer for two-classes. For multi-classes, how can I transfer?

Best.

Hi,
I tried to run the test_LA.py file and I got the following error:
FileNotFoundError: [Errno 2] No such file or directory: '../model/DTC_16labels/best_model.pth'
I wonder if you can help me.
Thanks

I want to visualize the predicted level set function in your figure1, could you tell me how to output it? I try to modify the test_util.py but failed.

I found you set k=1500 in DTC paper and actually k=-1500 in this code. Is there anything I haven't noticed ?

hi， author. The report is related with semi-supervision, but I can't find anything with unlabeled data.
I only see labeled data for training in Dataloader. In the code, how do you enter an unlabeled data into the network?
please give me some help. Thx.

Hi,
Does your code work on multi-label data?
Thank you in advance :)

Can this code be directly used for the segmentation of 2D images?
I would be very grateful if you could reply

cannot find the networks.discriminator file?

Hello, is it okay for me to train a liver dataset with this code? I lose drops when training but DICE barely when testing

Hello！
The evaluation index I reproduced is one point lower than that in the paper,
do you know the reason？

What does the 150 mean here? Did you find that value by trial and error or does one epoch consist of 150 iterations? I assume, that the value should be specific to the dataset and perhaps also the model?

Hi thanks a lot for releasing the code. On pancreas dataset, I have followed the same pipeline of preprocessing, including resampling, center crop and normalization but I can only get 40.86 dice trained with 12 labeled data and 60.43 dice trained with all training data. Could you please specify what augmentation you used in your paper? because after I remove rotation augmentation and use random crop only as on-the-fly augmentation, I can get a similar results as yours.

can you provide the code for processing pancreas? thanks

hi, I want to train the models with my own dataset，How can I achieve this objective？

there is "from networks.discriminator import FC3DDiscriminator" in the la_heart.py, but I do not see the discriminator file in the networks, and it seems that FC3DDiscriminator is not used in the training. So where should I use this? Looking forward to your reply.

During study, I am so interested in your network, Dual-task Consistency, and wondered the dataset configuration in training and testing.

My question is below:

• Patient numbers in 12 labeled training sets from 82 NIH-Pancreas datasets (ex. no. 1, no. 2, ...)

• Patient numbers in 20 testing sets from 82 NIH-Pancreas datasets (ex. no. 62, no. 63, ...)

• Ratio of training/validation dataset in network training

If you answer the above questions, I think it will be of great help to my study.

Many thanks =)

How to enter the unlabeled data and in which file is it entered, thank you very much