Polyp segmentation, a contentious issue in medical imaging, has seen numerous
proposed methods aimed at improving the quality of segmented masks. Currently,
state-of-the-art techniques yield impressive results. However, the sheer size
of these models poses challenges for practical industry applications. To
address this, we present a Knowledge Distillation framework, incorporating
attention supervision and the symmetrical guiding method. This framework is
designed to facilitate knowledge transfer from a teacher model to a more
compact student model with fewer parameters. Our experimental evaluation of the
framework assesses its effectiveness in enabling the student model to acquire
knowledge from the teacher efficiently. Additionally, our method serves to
prevent the student model from incorporating redundant features that could lead
to inaccurate predictions. Consequently, our method, boasting approximately 5
million parameters, achieves competitive results comparable to the
state-of-the-art approaches. The implementation can be found at:
this https URL

通过知识蒸馏、注意力监督和对称引导方法，实现从较大的教师模型向更小的学生模型进行知识转移，达到近似于最先进方法的竞争性结果。