  Deep learning is the current bet for image classification. Its greed for huge
amounts of annotated data limits its usage in medical imaging context. In this
scenario transfer learning appears as a prominent solution. In this report we
aim to clarify how transfer learning schemes may influence classification
results. We are particularly focused in the automated melanoma screening
problem, a case of medical imaging in which transfer learning is still not
widely used. We explored transfer with and without fine-tuning, sequential
transfers and usage of pre-trained models in general and specific datasets.
Although some issues remain open, our findings may drive future researches.
