Training Deep learning and validation loss
Using validation loss to determine when to stop iterating is a good strategy when you have a lot of labeled training data. However, in medical contexts, it's often the case that labeled data is expensive and/or difficult to come by. You mention that you only have 1000 samples -- this is a pretty small amount of data to be training a deep net with! Setting aside 100 or 200 images to form a validation set will probably hurt the model. You can use a validation set to get an estimate of how many epochs you should train for, and then train on the whole dataset for that many epochs.

In regards to your batch size query: I would recommend working with smaller batch sizes of around 32-128 images. In my experience, the networks converge more quickly when using smaller batch sizes.