This approach may serve as a first-stage, low-cost clinical stratification tool to identify high-risk individuals before more resource-intensive downstream screening such as pancreas-directed imaging as well as promising generalizability to an independent healthcare system.
Ji-Fan Gao, Mina Stanikic, Eshna Lahr et al.· Cancer Research· 0 citations
Attention-based multiple instance learning (ABMIL) using pathology foundation model embeddings is effective for slide-level tasks, but exhaustive inference requires applying a large image encoder to every foreground tile despite the subsequent attention distribution often concentrating over a small subset of informativ...
Duncan Stothers, Ren-Chin Wu, W. Lotter· 0 citations
BACKGROUND
Tumor mutational burden (TMB) has been established as a predictor of response to immune checkpoint inhibitors (ICI) ± chemotherapy in advanced non-small cell lung cancer (NSCLC). However, less is known about its role as a predictor of immune-related adverse events (irAEs) METHODS: This monocentric cohort stu...
F. Pecci, Xinan Wang, Valentina Santo et al.· Journal of Thoracic Oncology· 0 citations
Attention-based multiple instance learning (ABMIL) is the predominant approach for slide-level prediction in computational pathology, yet its attention maps provide only local explanations: they indicate where a model focuses but not which histological features drive its predictions or how the model behaves across a pa...
Abdallah Lamane, Abdul Rahman Diab, Ren-Chin Wu et al.· 0 citations
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