This paper presents a detailed analysis of different techniques to reduce memory consumption during the training stages in a Deep Learning algorithm for contrast enhancement in radiography. This makes it possible to work with large images, such as radiological images, avoiding the problems derived from working with patches or the loss of spatial resolution caused by subsampling. For this purpose, different approaches have been studied experimentally, both in single-GPU executions and in multi-GPU systems based on data and model parallelism. Experimental evaluation shows that it is possible to achieve up to 20% reduction for a single node and up to 70% for the distributed model without loss of accuracy.