Ultrasound images are widely used as a diagnosis support in the clinical field. This imaging modality has been recognized, over the years, as one of the most adaptable, thanks to its cost-effectiveness and portability, especially if compared to CT and MRI. Nevertheless, US also presents difficult challenges, such as the presence of noise and artifacts that reduce the quality of images, making the diagnosis substantially dependent on the radiologist’s skills and experience. Deep learning approaches have skyrocketed over the last years and have been applied to overcome these limits in different tasks involving ultrasounds, such as for classification, segmentation, and image quality assessment.