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, requiring extensive annotations. This study proposes an attention-based multiple instance learning (ABMIL) framework to predict the predominant LUAD growth pattern at the whole slide level to reduce annotation burden. Our approach integrates pretrained pathology foundation models as patch encoders, used either frozen or fine-tuned on annotated patches, to extract discriminative features that are aggregated through attention mechanisms. Experiments show that fine-tuned encoders improve performance, with Prov-GigaPath achieving the highest agreement (\k{appa} = 0.699) under ABMIL. Compared to simple patch-aggregation baselines, ABMIL yields more robust predictions by leveraging slide-level supervision and spatial attention. Future work will extend this framework to estimate the full distribution of growth patterns and validate performance on external cohorts.
基于注意力机制的多示例学习：利用基础模型预测肺腺癌全切片图像中的主要生长模式 / Attention-based multiple instance learning for predominant growth pattern prediction in lung adenocarcinoma wsi using foundation models
本研究提出一种基于注意力机制的多示例学习方法，通过整合预训练的病理基础模型，仅利用全切片级别的标注即可自动预测肺腺癌的主要生长模式，减轻了对大量精细标注的依赖，并取得了优于传统方法的预测性能。
源自 arXiv: 2604.21530
