Fig. 1: Model performance evaluation. | npj Digital Medicine

Fig. 1: Model performance evaluation.

From: UroFusion-X: a unified multimodal deep learning framework for robust diagnosis, subtyping, and prognosis of urological cancers

Fig. 1: Model performance evaluation.

a Receiver operating characteristic (ROC) curves across cancer types showing diagnostic discrimination capacity of UroFusion-X compared to unimodal and baseline multimodal methods. b Leave-one-center-out (LOCO) validation performance demonstrating robust cross-institutional generalization with performance degradation of 2–6% AUROC when each center is held out. c Calibration curves and expected calibration error (ECE) metrics showing reliable probability calibration across models and cancer types. d Multi-task learning performance profiles and technical factor impact analyses demonstrating complementary gains in diagnosis, subtyping, and survival tasks. e Subgroup performance heatmap (AUROC) across demographic and clinical attributes showing stable behavior across patient subgroups. f Comparison with radiologist diagnostic accuracy, inference time analysis, and confusion matrix of UroFusion-X showing competitive or superior performance.

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