KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866914767530098688 |
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| author | Trinh, Quoc-Huy Nguyen, Minh-Van Thi, Phuoc-Thao Vo |
| author_facet | Trinh, Quoc-Huy Nguyen, Minh-Van Thi, Phuoc-Thao Vo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_08555 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation Trinh, Quoc-Huy Nguyen, Minh-Van Thi, Phuoc-Thao Vo Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2312.08555 |