It can benefit any recommendation platform in a plug-and-play style.

Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data.

In this paper, we provide a framework for designing and analysing general U-Net architectures.

However, this is not always the case, as different tasks can compete with each other and lead to weaker models.