Fig. 6: Generalization to an independent patient cohort.
From: Histopathology-based protein multiplex generation using deep learning
a, Two examples (immune-high and -low) from the TCGA–SKCM cohort, showing H&E images, predicted protein multiplexes and expression profiles of MelanA, CD3 and CD20 markers. Scale bars, 2 mm (top) and 2.5 mm (bottom). b, Model architecture for multimodal survival and immune subtype prediction. c, Survival prediction results (i), displaying time-dependent C-index scores (left) and Kaplan–Meier survival curves for all test patients aggregated across five cross-validation folds for the multimodal setting, with separation of low- and high-risk groups (right). Immune subtype prediction results (ii), showing the weighted F1-score (left) and confusion matrix (right) for classification into low, intermediate and high immune subtypes. The confusion matrix corresponds to the fold with the highest weighted F1-score. For bar plots, bars represent mean values and error bars indicate s.d. Cls, classifier head; F, residual neural network feature extractor ResNet18; HX, H&E IMC features; HŶ, predicted IMC features.
