2 citations · 2 across the 11 of their papers we have counts for
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What Is Missing in Surgical Risk Stratification and Outcome Prediction: A Scoping Review of End-to-End Machine Learning Approaches
Yizhi Dong, Yuhe Ke, Hairil Rizal Abdullah +4
Postoperative adverse events, including mortality and morbidity, remain a major global burden, many of which are preventable through early identification of high-risk patients and…
Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities
Yucheng Xing, Hailan Mo, Zi Wang +2
Recent multimodal survival prediction models have demonstrated strong predictive performance by leveraging complementary information across modalities. However, such models general…
SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels
Jingying Ma, Feng Wu, Yucheng Xing +5
Electroencephalography (EEG) foundation models (EFMs) have shown strong potential for transferable representation learning, yet their adaptation in realistic settings remains chall…
CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model
Jingying Ma, Feng Wu, Qika Lin +4
Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation models (EFMs) have emerged to a…
Evidential time-to-event prediction with calibrated uncertainty quantification
Ling Huang, Yucheng Xing, Swapnil Mishra +2
Time-to-event analysis provides insights into clinical prognosis and treatment recommendations. However, this task is more challenging than standard regression problems due to the…
EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers
Ling Huang, Yucheng Xing, Qika Lin +2
Multimodal survival analysis aims to combine heterogeneous data sources (e.g., clinical, imaging, text, genomics) to improve the prediction quality of survival outcomes. However, t…
An evidential time-to-event prediction model based on Gaussian random fuzzy numbers
Ling Huang, Yucheng Xing, Thierry Denoeux +1
We introduce an evidential model for time-to-event prediction with censored data. In this model, uncertainty on event time is quantified by Gaussian random fuzzy numbers, a newly i…