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About splitting the data #1
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If you mean the first stage patch-level training, the answer is YES. In the patch-level training, we put all MSS patches no matter which patient they belong to in one folder. So as MSI. So that in patch-level we only have six folders: train/MSI, train/MSS, validation/MSI, validation/MSS, test/MSI, test/MSI. |
So for TCGA-AZ-4615 as an example in MSI test for CRC_DX, there are 61 pngs in the original dataset and I should put all of them in one folder for MSI test, right? |
Yes, just use your trained patch-level models to get the predicted scores of these 61 pngs. And you can use any aggregation method (MAg, counting, averaging and so on) to get the patient's predicted result (MSI or MSS). |
Sorry to bother you.why i find that there are 84 pngs of patient 'TCGA-AZ-4615' in the CRC_DX MSIMUT_test ?not 61 pngs. |
Hello, I checked my data again and I think in my experiment there is 84 pngs for TCGA-AZ-4615 in MSIMUT test. I think maybe you type the wrong id? Because I found there do have a patient with 61 pngs (TCGA-AZ-4315 in MSS test). |
I am sorry I don't know what's wrong with your split. I guess you have data loss when downloading and unzipping the dataset. |
How to split data according to your files? The code you gave, he uses all the patients for random splitting. There are several images that are corresponding to 1 patient, should I put all of them in the same folder?
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