All-in-SAM：从弱标注到基于提示微调的像素级细胞核分割
All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning
AI总结：
本文提出 all-in-SAM 流程，利用 SAM 从弱提示生成像素级标注并微调模型，在 Monuseg 数据集上超越 SOTA，且弱标注微调性能接近强标注。
摘要：
Segment Anything Model (SAM) 是一种最近提出的基于提示的分割模型，采用通用的零样本分割方法。凭借零样本分割能力，SAM 在各种分割任务中展现了令人印象深刻的灵活性和精确性。然而，当前的流程在推理阶段需要人工提示，这对于生物医学图像分割而言仍然耗费大量资源。在本文中，我们不再在推理阶段使用提示，而是引入了一种利用 SAM 的流程，称为 all-in-SAM，贯穿整个 AI 开发工作流（从标注生成到模型微调），在推理阶段无需人工提示。具体来说，首先使用 SAM 从弱提示（例如点、边界框）生成像素级标注。然后，使用这些像素级标注对 SAM 分割模型进行微调，而不是从头开始训练。我们的实验结果揭示了两个关键发现：1）所提出的流程在公共 Monuseg 数据集上的细胞核分割任务中超越了最先进（SOTA）方法；2）使用弱标注和少量标注对 SAM 进行微调，与使用强像素级标注数据相比，取得了具有竞争力的性能。
英文摘要：
The Segment Anything Model (SAM) is a recently proposed prompt-based segmentation model in a generic zero-shot segmentation approach. With the zero-shot segmentation capacity, SAM achieved impressive flexibility and precision on various segmentation tasks. However, the current pipeline requires manual prompts during the inference stage, which is still resource intensive for biomedical image segmentation. In this paper, instead of using prompts during the inference stage, we introduce a pipeline that utilizes the SAM, called all-in-SAM, through the entire AI development workflow (from annotation generation to model finetuning) without requiring manual prompts during the inference stage. Specifically, SAM is first employed to generate pixel-level annotations from weak prompts (e.g., points, bounding box). Then, the pixel-level annotations are used to finetune the SAM segmentation model rather than training from scratch. Our experimental results reveal two key findings: 1) the proposed pipeline surpasses the state-of-the-art (SOTA) methods in a nuclei segmentation task on the public Monuseg dataset, and 2) the utilization of weak and few annotations for SAM finetuning achieves competitive performance compared to using strong pixel-wise annotated data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
