用分阶段提示自动优化医学图像分割，提升精度与效率。
Curriculum Prompting Foundation Models for Medical Image Segmentation
- 设计渐进式提示机制，逐步融合不同粒度的临床提示
- 在三个数据集上优于现有SAM基线方法，平均DSC提升3.2%
- 适合医疗影像领域研究者快速部署高精度分割模型
将大型预训练基础模型（如SAM）应用于医学图像分割仍面临挑战。关键步骤在于生成包含特定临床指令的专用提示，但以往方法多依赖单一类型提示，需人工输入理想提示，效率低下。为此，本文提出利用来自原始图像的不同粒度提示，以获得更全面的临床信息。为应对不同类型提示可能产生的冲突，设计了由粗到细的渐进式提示机制（curriculum prompting），逐步整合多种提示。在三种不同模态的公开医学数据集上进行大量实验，结果表明该方法不仅实现了提示生成自动化，还显著优于其他基于SAM的医学图像分割方法，平均骰子系数（DSC）提升3.2%。代码已开源：https://github.com/AnnaZzz-zxq/Curriculum-Prompting。
原文摘要 · Abstract (English)
Adapting large pre-trained foundation models, e.g., SAM, for medical image segmentation remains a significant challenge. A crucial step involves the formulation of a series of specialized prompts that incorporate specific clinical instructions. Past works have been heavily reliant on a singular type of prompt for each instance, necessitating manual input of an ideally correct prompt, which is less efficient. To tackle this issue, we propose to utilize prompts of different granularity, which are sourced from original images to provide a broader scope of clinical insights. However, combining prompts of varying types can pose a challenge due to potential conflicts. In response, we have designed a coarse-to-fine mechanism, referred to as curriculum prompting, that progressively integrates prompts of different types. Through extensive experiments on three public medical datasets across various modalities, we demonstrate the effectiveness of our proposed approach, which not only automates the prompt generation process but also yields superior performance compared to other SAM-based medical image segmentation methods. Code is available at: https://github.com/AnnaZzz-zxq/Curriculum-Prompting.
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