No particular reason. I'm going to try different patch sizes. Regarding the batch size, right now I'm using 32. Discarding chunks without mass, I get a dice score of 0.8 approx. which isn't really great, but I still have many things to try. And I'm checking recall and precision (and specificity) at the end of each epoch.

I'm going to try a bigger patch size and change the data loader as you suggested. Thanks for your help!

The images are grayscale x-ray images that have masses in them. For example, one of the images may have a shape of (2000, 1500, 1). I extract chunks of (32, 32, 1) and use those chunks to train the segmentation network. The dice score and loss is calculated on the foreground, and there is only one class (mass). I'm going to change the data loader to use only chunks with at least some mass in them, although my guess is that then the trained model is going to find masses in empty chunks too. Thanks for your suggestions!