3 papers
cs.CV2026
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…
q-bio.BM2025
PLM-eXplain: Divide and Conquer the Protein Embedding Space
Jan van Eck, Dea Gogishvili, Wilson Silva +1
Protein language models (PLMs) have revolutionised computational biology through their ability to generate powerful sequence representations for diverse prediction tasks. However,…
cs.CV2025
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…