AI RESEARCH
Attention-based multiple instance learning for predominant growth pattern prediction in lung adenocarcinoma wsi using foundation models
arXiv CS.CV
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ArXi:2604.21530v1 Announce Type: new 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.