2 citations · 5 across the 15 of their papers we have counts for
15 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…
Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning
Qika Lin, Yifan Zhu, Bin Pu +14
The clinical adoption of artificial intelligence (AI) in medical diagnostics is critically hampered by its black-box nature, which prevents clinicians from verifying the rationale…
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…
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models
Qika Lin, Tianzhe Zhao, Kai He +5
Due to the presence of the natural gap between Knowledge Graph (KG) structures and the natural language, the effective integration of holistic structural information of KGs with La…
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…
Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey
Qika Lin, Yifan Zhu, Xin Mei +6
The rapid development of artificial intelligence has constantly reshaped the field of intelligent healthcare and medicine. As a vital technology, multimodal learning has increasing…
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…
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
Ling Huang, Su Ruan, Yucheng Xing +1
The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions repo…
Medical Image Segmentation with Belief Function Theory and Deep Learning
Ling Huang
Deep learning has shown promising contributions in medical image segmentation with powerful learning and feature representation abilities. However, it has limitations for reasoning…