Summary of Curriculum Prompting Foundation Models For Medical Image Segmentation, by Xiuqi Zheng et al.
Curriculum Prompting Foundation Models for Medical Image Segmentation
by Xiuqi Zheng, Yuhang Zhang, Haoran Zhang, Hongrui Liang, Xueqi Bao, Zhuqing Jiang, Qicheng Lao
First submitted to arxiv on: 1 Sep 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 paper presents a novel approach to adapting pre-trained foundation models, specifically the SAM model, for medical image segmentation. The challenge lies in formulating specialized prompts that incorporate clinical instructions. Current methods rely on a single prompt type per instance, requiring manual input of an ideal prompt, which is inefficient. To address this, the authors propose using prompts of varying granularity sourced from original images to provide broader clinical insights. However, combining different prompt types can lead to conflicts, which are mitigated by a designed coarse-to-fine mechanism called curriculum prompting. The approach automates prompt generation and achieves superior performance compared to other SAM-based medical image segmentation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical image segmentation is crucial for diagnosing diseases, but adapting pre-trained models requires specialized prompts. Currently, each instance needs a specific prompt type, which is time-consuming. Researchers have come up with a new way of creating prompts that work together seamlessly. This approach uses different types of prompts to provide more information and avoid conflicts. The method has been tested on three medical image datasets and shows better results than previous methods. |
Keywords
» Artificial intelligence » Image segmentation » Prompt » Prompting » Sam
