SDPM is a diffusion probabilistic model that generates continuous survival times and censoring indicators to estimate survival functions without parametric assumptions or time discretization.
Transformer-based deep survival analysis
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Pith papers citing it
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A radiomics-guided hybrid Vision Transformer integrates pixel embeddings with interpretable radiomic features in a multimodal Cox model for survival analysis, yielding competitive discrimination and clinically meaningful attention maps on COVID-19 chest X-ray data.
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SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis
SDPM is a diffusion probabilistic model that generates continuous survival times and censoring indicators to estimate survival functions without parametric assumptions or time discretization.
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Radiomics-Guided Vision Transformers for Survival Analysis
A radiomics-guided hybrid Vision Transformer integrates pixel embeddings with interpretable radiomic features in a multimodal Cox model for survival analysis, yielding competitive discrimination and clinically meaningful attention maps on COVID-19 chest X-ray data.