Summary of Graph Relation Distillation For Efficient Biomedical Instance Segmentation, by Xiaoyu Liu et al.
Graph Relation Distillation for Efficient Biomedical Instance Segmentation
by Xiaoyu Liu, Yueyi Zhang, Zhiwei Xiong, Wei Huang, Bo Hu, Xiaoyan Sun, Feng Wu
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach for efficient biomedical instance segmentation using graph relation distillation. It addresses the limitations of existing knowledge distillation methods by transferring not only instance-level features but also global relation information and pixel-level boundaries. The proposed method, called graph relation distillation, consists of two schemes: instance graph distillation (IGD) and affinity graph distillation (AGD). IGD transfers instance features and relations by enforcing instance graph consistency, while AGD captures structured knowledge of instance boundaries by ensuring pixel affinity consistency. Experimental results on various biomedical datasets show that the proposed approach enables student models with fewer parameters and faster inference times while achieving promising performance compared to teacher models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better from big computers without using too much processing power or memory. They developed a new way to teach smaller, more efficient computer models how to identify different things in medical images, like cells or organs. This is important because it can help doctors and researchers analyze these images faster and with less error. The new method uses graphs, which are like networks of connected dots, to transfer knowledge from larger, more powerful computers to the smaller ones. This allows the small models to learn quickly and accurately without using too much resources. |
Keywords
» Artificial intelligence » Distillation » Inference » Instance segmentation » Knowledge distillation
