16 citations · 22 across the 12 of their papers we have counts for
13 papers
Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning
Xueyao Wang, Xiuding Cai, Honglin Shang +2
Early warning of intraoperative adverse events plays a vital role in reducing surgical risk and improving patient safety. While deep learning has shown promise in predicting the si…
VitalBench: A Rigorous Multi-Center Benchmark for Long-Term Vital Sign Prediction in Intraoperative Care
Xiuding Cai, Xueyao Wang, Sen Wang +3
Intraoperative monitoring and prediction of vital signs are critical for ensuring patient safety and improving surgical outcomes. Despite recent advances in deep learning models fo…
Self Identity Mapping
Xiuding Cai, Yaoyao Zhu, Linjie Fu +2
Regularization is essential in deep learning to enhance generalization and mitigate overfitting. However, conventional techniques often rely on heuristics, making them less reliabl…
Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation
Yingkai Wang, Yaoyao Zhu, Xiuding Cai +3
Medical image segmentation plays a crucial role in clinical workflows, but domain shift often leads to performance degradation when models are applied to unseen clinical domains. T…
Semantic Data Augmentation Enhanced Invariant Risk Minimization for Medical Image Domain Generalization
Yaoyao Zhu, Xiuding Cai, Yingkai Wang +3
Deep learning has achieved remarkable success in medical image classification. However, its clinical application is often hindered by data heterogeneity caused by variations in sca…
Invariance Principle Meets Vicinal Risk Minimization
Yaoyao Zhu, Xiuding Cai, Yingkai Wang +3
Deep learning models excel in computer vision tasks but often fail to generalize to out-of-distribution (OOD) domains. Invariant Risk Minimization (IRM) aims to address OOD general…
MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting
Xiuding Cai, Xueyao Wang, Yaoyao Zhu +1
In recent years, Transformers have become the de-facto architecture for long-term time series forecasting (LTSF), yet they face challenges associated with the self-attention mechan…
MD-Dose: A diffusion model based on the Mamba for radiation dose prediction
Linjie Fu, Xia Li, Xiuding Cai +4
Radiation therapy is crucial in cancer treatment. Experienced experts typically iteratively generate high-quality dose distribution maps, forming the basis for excellent radiation…
BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification
Yaoyao Zhu, Xiuding Cai, Xueyao Wang +3
Data augmentation is a crucial regularization technique for deep neural networks, particularly in medical image classification. Mainstream data augmentation (DA) methods are usuall…
SP-DiffDose: A Conditional Diffusion Model for Radiation Dose Prediction Based on Multi-Scale Fusion of Anatomical Structures, Guided by SwinTransformer and Projector
Linjie Fu, Xia Li, Xiuding Cai +4
Radiation therapy serves as an effective and standard method for cancer treatment. Excellent radiation therapy plans always rely on high-quality dose distribution maps obtained thr…
Energy-Guided Diffusion Model for CBCT-to-CT Synthesis
Linjie Fu, Xia Li, Xiuding Cai +3
Cone Beam CT (CBCT) plays a crucial role in Adaptive Radiation Therapy (ART) by accurately providing radiation treatment when organ anatomy changes occur. However, CBCT images suff…
Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control
Xiuding Cai, Jiao Chen, Yaoyao Zhu +2
Automated anesthesia promises to enable more precise and personalized anesthetic administration and free anesthesiologists from repetitive tasks, allowing them to focus on the most…
Rethinking the Paradigm of Content Constraints in Unpaired Image-to-Image Translation
Xiuding Cai, Yaoyao Zhu, Dong Miao +2
In an unpaired setting, lacking sufficient content constraints for image-to-image translation (I2I) tasks, GAN-based approaches are usually prone to model collapse. Current solutio…