10 papers
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…
Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis
Yucheng Xing, Ling Huang, Pei Liu +5
Whole-slide images (WSIs) are widely used for computational cancer prognosis. However, most existing methods primarily focus on in-domain performance and fail to generalize across…
Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining
Yucheng Xing, Pei Liu, Jingying Ma +6
Multiple instance learning (MIL) is the dominant framework for whole-slide image analysis in computational pathology, typically combining a frozen patch encoder, a projection layer…
Beyond Semantics: An Evidential Reasoning-Aware Multi-View Learning Framework for Trustworthy Mental Health Prediction
Yucheng Ruan, Ling Huang, Qika Lin +2
Automated mental health prediction using textual data has shown promising results with deep learning and large language models. However, deploying these models in high-stakes real-…
DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
Yucheng Xing, Ling Huang, Jingying Ma +6
Pathology whole-slide images (WSIs) are widely used for cancer survival analysis because of their comprehensive histopathological information at both cellular and tissue levels, en…
Towards accurate and reliable ICU outcome prediction: a multimodal learning framework based on belief function theory using structured EHRs and free-text notes
Yucheng Ruan, Daniel J. Tan, See Kiong Ng +2
Accurate Intensive Care Unit (ICU) outcome prediction is critical for improving patient treatment quality and ICU resource allocation. Existing research mainly focuses on structure…
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…