I am completely new to this forum so apologize in advance if someone asks something similar (didn’t find any).
I am starting to work with whole slide histology images with different classification purposes (tumor type, subtype, etc). As some of you already know, the files that you manage are extremely high resolution (sometimes 100,000x100,000 pixels and 10GB+). So, as you imagine, I am tiling the image to 224x224 size patches BUT I am kind of stucked with the labeling and/or the patch prediction aggregation. Since I do not have enough time to manually curate every patch and see if the sample have or not tumor tissue, I am more prone to use the second approach (differently from the breast dataset in kaggle). In order to do this I found this approach linked from this post that could solve my problem (the whole git is based on PyTorch, so great point here).
However, I would like to know if this could be implemented using fastai library. Any advice on this?