While 'conventional' deep learning approaches rely on extensively labeled datasets to learn a given task in 'onego', other training paradigms have been developed to address the challenges posed by limited data scenarios. These strategies will herein be referred to as 'multistage', since they iteratively adapt to the information contained in multiple datasets or tasks (usually utilized in a stepwise manner for training) to improve model performance. The three most common multi-staged strategies are (Fig. ):