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ayrna / deep-ordinal-clm

Ordinal classification with Deep Learning using the CLM
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retinopathy database trainval.csv and test.csv file #1

Open liminghu opened 3 years ago

liminghu commented 3 years ago

I am interested in the ordinal classifier, and are studying it using the retinopathy database. I have downloaded https://www.kaggle.com/c/diabetic-retinopathy-detection/data

according to the source code: src/datasets2.py, we also need: self._df_trainval = pd.read_csv(os.path.join(DATASETS_DIR, 'retinopathy/data128/trainval.csv')) self._df_test = pd.read_csv(os.path.join(DATASETS_DIR, 'retinopathy/data128/test.csv'))

Do you mind sharing these two files?

Thanks.

victormvy commented 3 years ago

Hello,

I'm glad to see that you are interested in this ordinal classifier. You can find attached those csv files along with the python script that we used to create them. retinopathy.zip

liminghu commented 3 years ago

Thanks a lot. I checked the .py script you shared, I noticed that it was looking for .bmp files, but the original image files are in *.jpeg format. I also read your paper again: The images are resized to 128 by 128 pixels and rescaled to [0, 1] range. Can you share other scripts? I just want to follow the steps to duplicate your result.

Thanks.

victormvy commented 3 years ago

I have been looking for the script that we used to resize the images to 128x128 but I could not find it. However, I can provide you the resized images that I used (2.73GB zip). The [0,1] rescaling is done in the generator class that is used to load the images. I hope that you can replicate the experiments. Let me know if you need something else.

Dataset download: https://mega.nz/file/TSZDTKCT#jm4I5s-xahLJ0C3vsbtmswPcCJpaW69g4xJar5I02Ew

liminghu commented 3 years ago

Thanks. I will try it again.

liminghu commented 3 years ago

I just noticed that the repo used tensorflow v1.3 which needs lower version of CUDA: https://stackoverflow.com/questions/50622525/which-tensorflow-and-cuda-version-combinations-are-compatible My env is higher CUDA version, hard to make it work.

liminghu commented 3 years ago

I checked the Diabetic Retinopathy Detection leadboard, the leading QWK score is around 0.85~0.86 which is far above the scores reported in the paper: 0.58.

victormvy commented 3 years ago

You can achieve higher QWK score using higher resolution images. The original images are high resolution but we rescaled them to 128x128 due to our hardware limitations. In our work, we compared our method with other methods that used 128x128 images too.

liminghu commented 3 years ago

Thanks. I will try it. The winning solution is using a resolution of 300*~. https://github.com/btgraham/SparseConvNet/blob/kaggle_Diabetic_Retinopathy_competition/competitionreport.pdf