研究人员开发了一个基于注意力机制的多实例学习（ABMIL）框架，用于从全切片图像预测肺腺癌的生长模式。该方法通过整合预训练的病理基础模型作为块编码器，减少了对大量标注的需求。实验表明，微调这些编码器可以提高性能，在ABMIL框架下，Prov-GigaPath达到了0.699的Kappa分数，优于简单的聚合基线。 AI
影响 引入了一种新颖的医学图像分析框架，该框架减少了标注负担并提高了癌症生长模式的预测准确性。
排序理由 学术论文，详细介绍了使用基础模型进行医学图像分析的新框架。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
Lung adenocarcinoma (LUAD) grading depends on accurately identifying growth patterns, which are indicators of prognosis and can influence treatment decisions. Common deep learning approaches to determine the predominant pattern rely on patch-level classification or segmentation, …
Lung adenocarcinoma (LUAD) grading depends on accurately identifying growth patterns, which are indicators of prognosis and can influence treatment decisions. Common deep learning approaches to determine the predominant pattern rely on patch-level classification or segmentation, …