Summary of Fm-osd: Foundation Model-enabled One-shot Detection Of Anatomical Landmarks, by Juzheng Miao et al.
FM-OSD: Foundation Model-Enabled One-Shot Detection of Anatomical Landmarks
by Juzheng Miao, Cheng Chen, Keli Zhang, Jie Chuai, Quanzheng Li, Pheng-Ann Heng
First submitted to arxiv on: 7 Jul 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 proposed FM-OSD framework leverages visual foundation models to achieve accurate one-shot landmark detection in medical images without relying on extensive unlabeled data. By employing frozen image encoders and introducing dual-branch global and local feature decoders, the method increases feature resolution through coarse-to-fine processing. The decoders are trained using a distance-aware similarity learning loss that incorporates domain knowledge from a single template image. A novel bidirectional matching strategy is also developed to improve robustness and accuracy in scattered similarity map scenarios. Compared to state-of-the-art one-shot methods, FM-OSD demonstrates superior performance on two public anatomical landmark detection datasets while only requiring a single template image. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary One-shot landmark detection in medical images is important for efficiency, but most current methods rely on lots of unlabeled data. The authors propose a new way to do this using just one template image and some special computer vision models. They use a “foundation model” to help find the landmarks, and then add some extra steps to make it work well. This approach is tested on two big datasets and performs better than other methods that need lots of data. |
Keywords
» Artificial intelligence » One shot
