Polyp segmentation, a contentious issue in medical imaging, has seen numerous proposed methods aimed at improving the quality of segmented masks. While current state-of-the-art techniques yield impressive results, the size and computational cost of these models create challenges for practical industry applications. To address this challenge, we present KDAS, a Knowledge Distillation framework that incorporates attention supervision, and our proposed Symmetrical Guiding Module. This framework is designed to facilitate a compact student model with fewer parameters, allowing it to learn the strengths of the teacher model and mitigate the inconsistency between teacher features and student features, a common challenge in Knowledge Distillation, via the Symmetrical Guiding Module. Through extensive experiments, our compact models demonstrate their strength by achieving competitive results with state-of-the-art methods, offering a promising approach to creating compact models with high accuracy for polyp segmentation and in the medical imaging field. The implementation is available on https://github.com/huyquoctrinh/KDAS.

翻译：息肉分割作为医学影像中的一个争议性问题，已有多种方法被提出以提升分割掩码的质量。尽管当前最先进的技术取得了显著成果，但这些模型的规模和计算成本对实际工业应用构成了挑战。为解决这一问题，我们提出了KDAS——一种引入注意力监督的知识蒸馏框架，并设计了对称引导模块。该框架旨在促进参数量更少的紧凑型学生模型，使其能够学习教师模型的优势，并通过对称引导模块缓解知识蒸馏中教师特征与学生特征不一致的常见难题。大量实验表明，我们的紧凑型模型在实现与最先进方法相竞争的性能方面展现出其优势，为息肉分割及医学影像领域创建高精度紧凑型模型提供了可行方案。代码实现已发布于https://github.com/huyquoctrinh/KDAS。