20 citations · 38 across the 18 of their papers we have counts for
19 papers
EchoBridge: Long-Tail-Aware ECG-Echocardiography Text Alignment for Echocardiography-Derived Cardiac Findings
Xiaocheng Fang, Jieyi Cai, Guangkun Nie +9
Standardized echocardiography conclusions provide meaningful supervision for learning ECG representations of echocardiography-derived cardiac findings. Global ECG--text alignment m…
ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment
Xiaocheng Fang, Haoyu Wang, Jieyi Cai +14
Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly those digitized from ECG imag…
CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
Bo Liu, Di Dai, Jingwei Liu +5
Granger Causal Discovery (GCD) is fundamental for analyzing temporal dependencies in complex systems. However, existing neural GCD methods predominantly rely on a "one-size-fits-al…
ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation
Jiarui Jin, Haoyu Wang, Xingliang Wu +9
Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) remain unreliable for ECG interpret…
ECGFlowCMR: Pretraining with ECG-Generated Cine CMR Helps Cardiac Disease Classification and Phenotype Prediction
Xiaocheng Fang, Zhengyao Ding, Guangkun Nie +9
Cardiac Magnetic Resonance (CMR) imaging provides a comprehensive assessment of cardiac structure and function but remains constrained by high acquisition costs and reliance on exp…
AnyPPG: An ECG-Guided PPG Foundation Model Trained on Over 100,000 Hours of Recordings for Holistic Health Profiling
Guangkun Nie, Xiaocheng Fang, Gongzheng Tang +5
Photoplethysmography (PPG) is widely used as a non-invasive and accessible modality for continuous health monitoring. However, despite being a peripheral hemodynamic signal intrins…
Self-Alignment Learning to Improve Myocardial Infarction Detection from Single-Lead ECG
Jiarui Jin, Xiaocheng Fang, Haoyu Wang +5
Myocardial infarction is a critical manifestation of coronary artery disease, yet detecting it from single-lead electrocardiogram (ECG) remains challenging due to limited spatial i…
PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection
Xiaocheng Fang, Jiarui Jin, Haoyu Wang +8
Electrocardiography (ECG) is the clinical gold standard for cardiovascular disease (CVD) assessment, yet continuous monitoring is constrained by the need for dedicated hardware and…
UniECG: Understanding and Generating ECG in One Unified Model
Jiarui Jin, Haoyu Wang, Xiang Lan +3
Electrocardiogram (ECG) interpretation is a fundamental skill in medical education, yet students often need more than static examples to connect waveform evidence with diagnostic r…
ECGTwin: Personalized ECG Generation Using Controllable Diffusion Model
Yongfan Lai, Bo Liu, Xinyan Guan +3
Personalized electrocardiogram (ECG) generation is to simulate a patient's ECG digital twins tailored to specific conditions. It has the potential to transform traditional healthca…
Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model
Jiarui Jin, Haoyu Wang, Hongyan Li +3
Electrocardiogram (ECG) is essential for the clinical diagnosis of arrhythmias and other heart diseases, but deep learning methods based on ECG often face limitations due to the ne…
DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information
Yongfan Lai, Jiabo Chen, Deyun Zhang +4
Heart disease remains a significant threat to human health. As a non-invasive diagnostic tool, the electrocardiogram (ECG) is one of the most widely used methods for cardiac screen…
Retrieval-Augmented Diffusion Models for Time Series Forecasting
Jingwei Liu, Ling Yang, Hongyan Li +1
While time series diffusion models have received considerable focus from many recent works, the performance of existing models remains highly unstable. Factors limiting time series…
Review of Data-centric Time Series Analysis from Sample, Feature, and Period
Chenxi Sun, Hongyan Li, Yaliang Li +1
Data is essential to performing time series analysis utilizing machine learning approaches, whether for classic models or today's large language models. A good time-series dataset…
Curriculum Design Helps Spiking Neural Networks to Classify Time Series
Chenxi Sun, Hongyan Li, Moxian Song +2
Spiking Neural Networks (SNNs) have a greater potential for modeling time series data than Artificial Neural Networks (ANNs), due to their inherent neuron dynamics and low energy c…
Curricular and Cyclical Loss for Time Series Learning Strategy
Chenxi Sun, Hongyan Li, Moxian Song +2
Time series widely exists in real-world applications and many deep learning models have performed well on it. Current research has shown the importance of learning strategy for mod…
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
Chenxi Sun, Hongyan Li, Yaliang Li +1
This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a fundamental large…
Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks
Chenxi Sun, Hongyan Li, Moxian Song +3
Continuous diagnosis and prognosis are essential for intensive care patients. It can provide more opportunities for timely treatment and rational resource allocation, especially fo…
Confidence-Guided Learning Process for Continuous Classification of Time Series
Chenxi Sun, Moxian Song, Derun Can +3
In the real world, the class of a time series is usually labeled at the final time, but many applications require to classify time series at every time point. e.g. the outcome of a…