I'm training a Unet model for tumor segmentation. I have a dataset of 400 patients for that. The used images are CT scans (3D images) that I divide into 2D images (for a total of 30k 2D images).
I am actually splitting the dataset into: 10% test data, 18% validation data, 72% actual training data. I'm dividing the test and training data over patients (i.e. the patients used for testing are not the same as the one for training). Afterwards, I shuffle the 2D images and split in training/testing dataset (i.e. the same patients can be found in training dataset and validation dataset but not same stack images).
I have two questions:
- Should I split the train/validation dataset according to patients too ?
- Are the division percentages in train/test/validation adapted for my problem ?