  Radiation therapy treatment planning requires balancing the delivery of the
target dose while sparing normal tissues, making it a complex process. To
streamline the planning process and enhance its quality, there is a growing
demand for knowledge-based planning (KBP). Ensemble learning has shown
impressive power in various deep learning tasks, and it has great potential to
improve the performance of KBP. However, the effectiveness of ensemble learning
heavily depends on the diversity and individual accuracy of the base learners.
Moreover, the complexity of model ensembles is a major concern, as it requires
maintaining multiple models during inference, leading to increased
computational cost and storage overhead. In this study, we propose a novel
learning-based ensemble approach named LENAS, which integrates neural
architecture search with knowledge distillation for 3D radiotherapy dose
prediction. Our approach starts by exhaustively searching each block from an
enormous architecture space to identify multiple architectures that exhibit
promising performance and significant diversity. To mitigate the complexity
introduced by the model ensemble, we adopt the teacher-student paradigm,
leveraging the diverse outputs from multiple learned networks as supervisory
signals to guide the training of the student network. Furthermore, to preserve
high-level semantic information, we design a hybrid-loss to optimize the
student network, enabling it to recover the knowledge embedded within the
teacher networks. The proposed method has been evaluated on two public
datasets, OpenKBP and AIMIS. Extensive experimental results demonstrate the
effectiveness of our method and its superior performance to the
state-of-the-art methods.
