I am quite new to using PyTorch, and I’m currently researching ways to create a custom dataset for my issue. I currently have around 287 patients, and I was able to preprocess the DICOM files to create images and mask files for all of them.
The problem I’m currently facing is as follows: I would like to train my UNET model on all the slices for each patient that’s in the train group. I currently a root directory called data, which consists of two folders, called images and masks respectively. Both folders contain 287 patient folders and within each patient folder, includes the same n amount of MRI slices. The task I’m currently trying to work on is semantic segmentation using UNET, and from what I can see -
ImageFolder only works for image classification.
What would be the best way to create a custom dataset for this, and how would I load the data to my UNET model? Please let me know! Thank you in advance!