ymirsky / ct-gan Goto Github PK

Thank you @ymirsky . I just watched your video as well. Do you have any notes on the hardware specs you used for training?

Hello Dr.mirsky.
I tried implement your code for creating injector and remover models.
I used world coord for 169 sample E.g :
filename | z | x | y
LIDC-IDRI-0003/1-001.dcm | -169.6239465 | -47.40284557 | -30.171209
LIDC-IDRI-0011/1-001.dcm | -67.08815205 | -72.97101044 | 49.02652606
LIDC-IDRI-0012/1-001.dcm | -196.1703197 | 92.33199711 | 0.471155965
LIDC-IDRI-0013/1-001.dcm | -124.4263249 | -36.51308198 | -65.21501284
LIDC-IDRI-0014/1-001.dcm | -135.9209832 | 59.49693039 | -6.696593702
for creating injector model.

After completing the generator training process, the D loss, acc and G loss reach :

[Epoch 199/200] [Batch 342/347] [D loss: 0.000427, acc: 100%] [G loss: 4.560094] time: 1 day, 4:55:22.522646
[Epoch 199/200] [Batch 343/347] [D loss: 0.000721, acc: 100%] [G loss: 4.883993] time: 1 day, 4:55:24.019246
[Epoch 199/200] [Batch 344/347] [D loss: 0.000610, acc: 100%] [G loss: 5.273649] time: 1 day, 4:55:25.515293
[Epoch 199/200] [Batch 345/347] [D loss: 0.001337, acc: 100%] [G loss: 4.006495] time: 1 day, 4:55:27.011006

When using the generator model to inject into a new scan by running the 3A_inject_evidence.py , after adding noise touch ups in the attack_pipeline.py

In output i receive these runtime warnings :

Adding noise touch-ups...
/home/Desktop/final2/env/lib/python3.8/site-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice
  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
/home/Desktop/final2/env/lib/python3.8/site-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide
  arrmean = um.true_divide(
/home/Desktop/final2/env/lib/python3.8/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
/home/Desktop/final2/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice.
  return _methods._mean(a, axis=axis, dtype=dtype,
/home/Desktop/final2/env/lib/python3.8/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
touch-ups complete

And as a result of the injection process, some saved scan looks like this:

For some scans, I don't receive the runtime warnings and the injection is applied to the saved scan.
Can you help me with this?

Hi，
I am recreating your network and I have a problem with training.
To save time, I reduced the number of enhanced pictures by rotation to 4 and reduced the number of filters to 32.
When I trained for 200 epochs, my results are as follows:
D_loss acc G_loss
0.004051 | 100 | 6.248324 | 2 days, 12:04:16.416641
0.003934 | 100 | 5.108233 | 2 days, 12:04:35.168347
0.010535 | 100 | 4.973368 | 2 days, 12:04:53.246595
0.004152 | 100 | 5.723831 | 2 days, 12:05:10.234157
0.006562 | 100 | 5.827019 | 2 days, 12:05:28.685273
0.003435 | 100 | 5.879334 | 2 days, 12:05:46.087877
I want to know if your results are similar to me.
Because GAN expects the acc of D to be 50%, and the loss of G is as small as possible，this result does not meet expectations.
Do you have a way to optimize your network?
Your work has helped me a lot.
Thanks,
Best wishes.

Hello Dr.mirsky.
I have a problem in world2vox method and this line

orientation value is [1. 0. 0. 0. 1. 0.] and spacing value is in format [z , y , x] which after np.diag that value is

[[z 0 0]
 [0 y 0]
 [0 0 x]]

so in np.dot(orientation, np.diag(spacing)) we cannot calculate np.dot because orientation and spacing cannot be multiplied and then throw exception with this message:
ValueError: shapes (6,) and (3,3) not aligned: 6 (dim 0) != 3 (dim 0)
Can you help me about this?

Hello, I would like to ask why you didn't use any activation function after the last convolutional layer in the discriminator.

def build_discriminator(self):
    def d_layer(layer_input, filters, f_size=4, bn=True):
        """Discriminator layer"""
        d = Conv3D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
        d = LeakyReLU(alpha=0.2)(d)
        if bn:
            d = BatchNormalization(momentum=0.8)(d)
        return d

    img_A = Input(shape=self.img_shape)
    img_B = Input(shape=self.img_shape)

    # Concatenate image and conditioning image by channels to produce input
    model_input = Concatenate(axis=-1)([img_A, img_B])

    d1 = d_layer(model_input, self.df, bn=False)
    d2 = d_layer(d1, self.df * 2)
    d3 = d_layer(d2, self.df * 4)
    d4 = d_layer(d3, self.df * 8)

    validity = Conv3D(1, kernel_size=4, strides=1, padding='same')(d4)

    return Model([img_A, img_B], validity)

Also the shape of validity is (2,2,2,1) which is not clear to me. Shouldn't it be just one neuron -> real or fake like its depicted in your publication in figure 6?

Dear doctor,I don't know why, I have already finish the file of config.py

I have a problem with output "3_A_inject_evidence.py"
After running "3_A_inject_evidence.py", the DICOM output on the online DICOM viewer is shown below:

Can you help me about this?