I'm working on a deep learning project and have encountered a problem. The images that I'm using are very large and extremely detailed. They also contain a huge amount of necessary visual information, so it's hard to downgrade the resolution. I've gotten around this by slicing my images into 'tiles,' with resolution 512 x 512. There are several thousand tiles for each image.
Here's the problem—the annotations are binary and the images are heterogenous. Thus, an annotation can be applied to a tile of the image that has no impact on the actual classification. How can I lessen the impact of tiles that are 'improperly' labeled.
One thought is to cluster the tiles with something like a t-SNE plot and compare the ratio of the binary annotations for different regions (or 'classes'). I could then assign weights to images based on where it's located and then use that as an extra layer in my training. Very new to all of this, so wouldn't be surprised if that's an awful idea! Just thought I'd take a stab.
For background, I'm using transfer learning on Inception v3.