Extended Data Fig. 9: Alignment between pathology reports and images.
From: A whole-slide foundation model for digital pathology from real-world data
a-d, Bar plots showing the performance of f1 (a), Precision (b), AUROC (c) and AUPRC (d) using fine-tuned Prov-GigaPath to predict mutations in the zero-shot learning setting. The error bars show the standard error across n = 50 experiments and the bar centre shows the mean value. The listed p-value indicates the significance level that Prov-GigaPath outperforms the best comparison approach, with one-sided Wilcoxon test. e, Scatter plots comparing Prov-GigaPath and MI-Zero on cancer subtyping prediction and mutation prediction in terms of balanced accuracy (BACC).