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Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction
Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur +4
While multimodal survival prediction models are increasingly more accurate, their complexity often reduces interpretability, limiting insight into how different data sources influe…
Leveraging Adversarial Learning for Pathological Fidelity in Virtual Staining
José Teixeira, Pascal Klöckner, Diana Montezuma +5
In addition to evaluating tumor morphology using H&E staining, immunohistochemistry is used to assess the presence of specific proteins within the tissue. However, this is a costly…
Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction
Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur +3
To improve the prediction of cancer survival using whole-slide images and transcriptomics data, it is crucial to capture both modality-shared and modality-specific information. How…