Efficient medical image segmentation aims to provide accurate pixel-wise predictions for medical images with a lightweight implementation framework. However, lightweight frameworks generally fail to achieve superior performance and suffer from poor generalizable ability on cross-domain tasks. In this paper, we explore the generalizable knowledge distillation for the efficient segmentation of cross-domain medical images. Considering the domain gaps between different medical datasets, we propose the Model-Specific Alignment Networks (MSAN) to obtain the domain-invariant representations. Meanwhile, a customized Alignment Consistency Training (ACT) strategy is designed to promote the MSAN training. Considering the domain-invariant representative vectors in MSAN, we propose two generalizable knowledge distillation schemes for cross-domain distillation, Dual Contrastive Graph Distillation (DCGD) and Domain-Invariant Cross Distillation (DICD). Specifically, in DCGD, two types of implicit contrastive graphs are designed to represent the intra-coupling and inter-coupling semantic correlations from the perspective of data distribution. In DICD, the domain-invariant semantic vectors from the two models (i.e., teacher and student) are leveraged to cross-reconstruct features by the header exchange of MSAN, which achieves improvement in the generalization of both the encoder and decoder in the student model. Furthermore, a metric named Frechet Semantic Distance (FSD) is tailored to verify the effectiveness of the regularized domain-invariant features. Extensive experiments conducted on the Liver and Retinal Vessel Segmentation datasets demonstrate the superiority of our method, in terms of performance and generalization on lightweight frameworks.
翻译:高效医疗图像分割旨在为具有轻量度执行框架的医疗图像提供准确的像素预言。 但是, 轻量框架通常未能达到优异性, 且在跨域任务方面缺乏一般化能力。 在本文中, 我们探索了用于跨域医学图像高效分解的通用知识蒸馏法。 考虑到不同医学数据集之间的域差, 我们提议了模型分辨匹配网络( MSAN), 以获得域- 异差表示。 同时, 定制的对齐调调调调和一致性培训( ACT) 战略旨在促进 MISN 培训。 考虑到 MISN 的域- 利差性代表矢量化工具, 我们提出了两种通用知识蒸馏计划, 用于跨域蒸馏、 双相对比性图形蒸馏( DCGD) 和 杜内变性交叉蒸馏( DCGD) 。 具体来说, 在DCDGD, 两种普通对比图解式图示中, 代表了学生内部相交错调和交义性关系, 从两个方向的流流- 数据分配中, 将SeCD- dealder- degrealcregreal- decomdecal decreal decreal decal decional decation des the sal degildal deal deal deal deal degmentald the smamental deal deal deal des the smamentalmentalmentalmentaldaldaldaldaldald the smadaldaldaldaldaldald the smamentaldaldaldaldald mament madald the sald the saldald the sald the saldaldaldald madaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldald 和制成制成一种, 和制成的师制成制成成成的师制成成, 和制制制制制制成, 和制制制制制制制制制制制制成的师制制制制制制制制制制制