Papers › KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation
KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation
Quoc-Huy Trinh, Minh-Van Nguyen, Phuoc-Thao Vo Thi
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Medical Image Segmentation | CVC-ClinicDB | KDAS | mIoU | 0.872 | #33 of 48 | Archive leaderboard | report |
| Medical Image Segmentation | CVC-ClinicDB | KDAS | mean Dice | 0.925 | #33 of 48 | Archive leaderboard | report |
| Medical Image Segmentation | CVC-ColonDB | KDAS | Average MAE | 0.032 | #20 of 25 | Archive leaderboard | report |
| Medical Image Segmentation | CVC-ColonDB | KDAS | mIoU | 0.679 | #20 of 25 | Archive leaderboard | report |
| Medical Image Segmentation | CVC-ColonDB | KDAS | mean Dice | 0.759 | #20 of 25 | Archive leaderboard | report |
| Medical Image Segmentation | Kvasir-SEG | KDAS | Average MAE | 0.027 | #32 of 58 | Archive leaderboard | report |
| Medical Image Segmentation | Kvasir-SEG | KDAS | mIoU | 0.848 | #32 of 58 | Archive leaderboard | report |
| Medical Image Segmentation | Kvasir-SEG | KDAS | mean Dice | 0.913 | #32 of 58 | Archive leaderboard | report |
| Polyp Segmentation | Kvasir-SEG | KDAS | mDice | 0.913 | #2 of 8 | Archive leaderboard | report |
| Polyp Segmentation | Kvasir-SEG | KDAS | mIoU | 0.848 | #2 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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