# Stain-to-Stain Translation
Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis
# Structure
**step 1: pre-processing**
Create paired patches:
![y](https://user-images.githubusercontent.com/27900037/72141237-b3f5b200-33a7-11ea-936a-45393dada126.png)
**step 2: stain-to-stain translation model**
![model](https://user-images.githubusercontent.com/27900037/72138589-557a0500-33a2-11ea-9567-c3f88c1184ab.png)
**step 3: using_stst**
The two models of 'model-17-2200' and 'model-11-2400' are included in Google Drive. You can download them via this link.
https://drive.google.com/drive/folders/1-ZIFlba3oz5fas1tqup6Cbm7VWL3jeXm
**step 4: post-processing**
**step 5: evaluation**
![h](https://user-images.githubusercontent.com/27900037/72147740-777c8300-33b4-11ea-9146-cfc6fe0252bf.png)
# Plot discriminator and generator loss
![1](https://user-images.githubusercontent.com/27900037/72138096-4d6d9580-33a1-11ea-8466-ba227c210011.png)
![2](https://user-images.githubusercontent.com/27900037/72138104-52cae000-33a1-11ea-9245-b1368bf0c0e8.png)
![3](https://user-images.githubusercontent.com/27900037/72138109-552d3a00-33a1-11ea-97cb-0f4a77cfca50.png)
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Stain-to-Stain-Translation论文代码
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m:25个
dll:15个
py:11个
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2024-03-19
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Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis
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Stain-to-Stain-Translation-master.zip (72个子文件)
Stain-to-Stain-Translation-master
2_model
data_loader.py 2KB
stain-to-stain-translation.py 5KB
define_discriminator.py 2KB
define_generator.py 3KB
plot_disc_gen_loss
Disc_loss_fake.npy 352KB
Disc_loss_real.npy 352KB
plot_disc_gen_loss.py 3KB
Gen_loss.npy 352KB
full_ref_eva.ipynb 6KB
5_evaluation
evaluate.m 2KB
FeatureSIM.m 18KB
euclidean_distance.py 774B
msssim.m 6KB
Pearson_Cor_Coef.py 885B
Macenko.ipynb 16KB
7.tiff 194KB
Vahadane.ipynb 35KB
1_preprocessing
train_test_random.py 399B
create_paired_patches.py 876B
1.tiff 384KB
mitos-extract_patches.py 958B
README.md 1KB
4_postprocessing
stain_normalisation_toolbox
mingw.mlpkginstall 15KB
demo.m 4KB
PseudoColourStains.m 3KB
README.txt 6KB
StainMatrixEstimation
EstUsingSCD.m 3KB
EstUsingMacenko.m 2KB
Deconvolve.m 3KB
bin
LeedsSCD
opencv_objdetect220.dll 317KB
msvcp100.dll 593KB
opencv_features2d220.dll 834KB
opencv_contrib220.dll 359KB
opencv_highgui220.dll 931KB
opencv_video220.dll 264KB
vcomp100.dll 56KB
opencv_legacy220.dll 644KB
opencv_core220.dll 2.63MB
opencv_flann220.dll 26KB
ColourNormalisation.exe 275KB
opencv_ml220.dll 430KB
opencv_gpu220.dll 125KB
opencv_calib3d220.dll 497KB
opencv_imgproc220.dll 1.6MB
HE.colourmodel 742KB
msvcr100.dll 808KB
StainNormalisation
NormReinhard.m 3KB
NormMacenko.m 3KB
NormRGBHist.m 2KB
Norm.m 4KB
NormSCD.m 8KB
NormSCDLeeds.m 2KB
mingw-get-setup.exe 91KB
install.m 546B
Images
Ref.png 313KB
Source_small.png 343KB
colour_deconvolution.c 17KB
SCDTraining
MakeClassifier.m 5KB
Default
DefaultStainTrainingStruct.mat 2.45MB
ClassifyRF.m 1KB
TrainRF.m 761B
ClassifyStainRegions.m 3KB
BuildImageFeatureVector.m 1KB
README.txt 2KB
otq.m 6KB
ComputeSCDs.m 3KB
GenerateHistogram.m 2KB
stain_separation.m 780B
stain_norm_khan_mackenko_reinhard.m 1KB
Reinhard.ipynb 10KB
3_using_stst
test_stst.py 2KB
README.txt 205B
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