KDAS3: Knowledge distillation via Attention Supervision, and Symmetrical structure guiding for Polyp Segmentation

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: https://github.com/huyquoctrinh/KDAS3