Fig. 4: Robustness to missing modalities. | npj Digital Medicine

Fig. 4: Robustness to missing modalities.

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

Fig. 4: Robustness to missing modalities.The alternative text for this image may have been generated using AI.

a Degradation of diagnostic AUROC under removal of imaging (2–3% decrease), pathology (3–4% decrease), or genomics (2–3% decrease), with all exceeding 90% retention target. b Case study illustrating missing imaging scenario with reduced lesion localization confidence but preserved tissue morphology recognition. c Case study showing missing pathology leads to subtype over-calls and compensatory reliance on genomic and imaging signals. d Case study demonstrating missing genomics results in mis-stratification of biologically aggressive subtypes and reduced prognostic discrimination. e Modality contribution analysis showing imaging 40% for diagnosis, pathology 35% for subtyping, genomics 45% for survival prediction. f Componentwise ablation quantifying gated PoE effectiveness: 2.8% average degradation with gating vs 8.5% with concatenation, demonstrating gated fusion is essential for robustness.

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