随着深度卷积神经网络的发展，医学图像分割在最近几年取得了一系列的突破。然而，高性能的卷积神经网络总是意味着众多的参数和高计算成本，这将阻碍资源有限的医学场景中的应用。同时，大规模标注医学图像数据集的稀缺性进一步阻碍了高性能网络的应用。为了解决这些问题，我们提出了一个全面的知识蒸馏框架——Graph Flow，用于网络效率和标注效率的医学图像分割。具体而言，Graph Flow蒸馏从一个经过良好训练的笨重教师网络中转移跨层变化的精髓到一个未经训练的紧凑学生网络。此外，我们还集成了一个无监督的Paraphraser模块，以净化教师的知识，这对于训练稳定性也是有益的。此外，我们通过集成敌对蒸馏和香草对数蒸馏构建了一个统一的蒸馏框架，可以进一步优化紧凑网络的最终预测。我们使用不同的教师网络（传统卷积结构或流行的变换器结构）和学生网络，在四个具有不同模态（胃癌，突触，BUSI和CVC-ClinicDB）的医学图像数据集上进行了大量实验。我们展示了我们的方法在这些数据集上的突出能力，实现了竞争性能。此外，通过一种新颖的半监督范例，我们证明了我们的Graph Flow的有效性，用于双重高效的医学图像分割。我们的代码将在Graph Flow上提供。

With the development of deep convolutional neural networks, medical image segmentation has achieved a series of breakthroughs in recent years. However, high-performance convolutional neural networks always mean numerous parameters and high computation costs, which will hinder the applications in resource-limited medical scenarios. Meanwhile, the scarceness of large-scale annotated medical image datasets further impedes the application of high-performance networks. To tackle these problems, we propose Graph Flow, a comprehensive knowledge distillation framework, for both network efficiency and annotation-efficiency medical image segmentation. Specifically, the Graph Flow Distillation transfers the essence of cross-layer variations from a well trained cumbersome teacher network to a non-trained compact student network. In addition, an unsupervised Paraphraser Module is integrated to purify the knowledge of the teacher, which is also beneficial for the training stabilization. Furthermore, we build a unified distillation framework by integrating the adversarial distillation and the vanilla logits distillation, which can further refine the final predictions of the compact network. With different teacher networks (traditional convolutional architecture or prevalent transformer architecture) and student networks, we conduct extensive experiments on four medical image datasets with different modalities (Gastric Cancer, Synapse, BUSI, and CVC-ClinicDB).We demonstrate the prominent ability of our method on these datasets, which achieves competitive performances. Moreover, we demonstrate the effectiveness of our Graph Flow through a novel semi-supervised paradigm for dual efficient medical image segmentation. Our code will be available at Graph Flow.