Current browse context:
eess.IV
Change to browse by:
References & Citations
Electrical Engineering and Systems Science > Image and Video Processing
Title: KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation
(Submitted on 13 Dec 2023 (v1), last revised 23 Apr 2024 (this version, v3))
Abstract: 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 this https URL
Submission history
From: Quoc-Huy Trinh [view email][v1] Wed, 13 Dec 2023 23:00:48 GMT (7638kb,D)
[v2] Wed, 17 Apr 2024 08:38:54 GMT (10710kb,D)
[v3] Tue, 23 Apr 2024 19:31:25 GMT (10711kb,D)
Link back to: arXiv, form interface, contact.