In recent years, the medical field has benefited greatly from the use of machine learning technologies and Deep Learning in particular. More and more tasks are now being performed using these techniques, such as classification and segmentation of lesions, in order to drastically reduce the radiologist's time and effort. Although these algorithms are extremely effective, they require a huge amount of data to generalize due to the diversity of modalities, organs, acquisition devices and clinical tasks. This problem is not trivial, considering that the data must be tagged, which requires a huge investment of time and resources by doctors or radiologists.