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推动视觉语言模型用于核心实例分割和分类

Jieru Yao, Guangyu Guo, Zhaohui Zheng

IEEE transactions on medical imaging

|June 25, 2025

概括

PromptNu通过利用视觉语言模型 (VLM) 和提示工程来增强整个幻灯片成像 (WSI) 中的核实例细分和分类. 这种新的框架减少了对准确核检测的手动注释的依赖.

科学领域:

数字病理学数字病理学
计算生物学计算生物学
人工智能的人工智能

背景情况:

核实例细分和分类在整个幻灯片成像 (WSI) 分析中至关重要,但具有挑战性.
当前的方法通常需要大量的手动注释,需要大量的时间和专业知识.
视觉语言模型 (VLMs) 通过从大规模的图像文本数据中学习,而无需繁的标签,提供了一个有希望的替代方案.

研究的目的:

引入PromptNu,这是一个用于核实例识别的新框架.
在模型培训中注入全面的核心知识,使用视觉语言对比学习和提示工程.
在WSI分析中克服传统注释依赖方法的局限性.

主要方法:

开发了多方面的提示,整合了视觉,统计和专家核知识.
提议促进核表示学习 (PNuRL) 用于特征提取.
引入了促进核密度预测 (PNuDP) 以整合VLM专业知识用于预测.
利用视觉语言对比学习来增强核实例识别.

主要成果:

在六个不同的数据集中证明了PromptNu的有效性.
在核实例细分和分类任务中取得了显著的改进.
在各种整个幻灯片成像场景中验证了框架的性能.

结论:

在WSI中,PromptNu成功地整合了VLM知识和用于核分析的Prompt工程.
拟议的方法提供了一个高效和有效的替代手动注释驱动的方法.
该框架显示了推进自动化数字病理学的巨大潜力.

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