79 citations · 299 across the 37 of their papers we have counts for
37 papers
MammoExpert: Benchmarking Chain-of-Thought Reasoning in Mammography Diagnosis
Di Dai, Bo Liu, Youcheng Li +9
Mammography is an essential tool for breast cancer detection, with millions of examinations conducted annually. However, publicly available high-quality mammography datasets for AI…
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…
MieDB-100k: A Comprehensive Dataset for Medical Image Editing
Yongfan Lai, Wen Qian, Bo Liu +5
The scarcity of high-quality data remains a primary bottleneck in adapting multimodal generative models for medical image editing. Existing medical image editing datasets often suf…
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…
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…
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…
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…
Frozen Language Model Helps ECG Zero-Shot Learning
Jun Li, Che Liu, Sibo Cheng +2
The electrocardiogram (ECG) is one of the most commonly used non-invasive, convenient medical monitoring tools that assist in the clinical diagnosis of heart diseases. Recently, de…
Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images
Deyun Zhang, Shijia Geng, Yang Zhou +28
The artificial intelligence (AI) system has achieved expert-level performance in electrocardiogram (ECG) signal analysis. However, in underdeveloped countries or regions where the…
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
Xiang Lan, Hanshu Yan, Shenda Hong +1
Not all positive pairs are beneficial to time series contrastive learning. In this paper, we study two types of bad positive pairs that can impair the quality of time series repres…
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…
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
Ling Yang, Shenda Hong
Unsupervised/self-supervised graph representation learning is critical for downstream node- and graph-level classification tasks. Global structure of graphs helps discriminating re…
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training
Jiahao Shao, Shijia Geng, Zhaoji Fu +3
In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders. Owing to the development of technology and the increase in health awareness, ECG signals a…
MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs
Wenrui Zhang, Shijia Geng, Zhaoji Fu +3
Ventricular arrhythmias (VA) are the main causes of sudden cardiac death. Developing machine learning methods for detecting VA based on electrocardiograms (ECGs) can help save peop…
A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings
Wenrui Zhang, Xinxin Di, Guodong Wei +3
With the development of deep learning-based methods, automated classification of electrocardiograms (ECGs) has recently gained much attention. Although the effectiveness of deep ne…
A Simple Self-Supervised ECG Representation Learning Method via Manipulated Temporal-Spatial Reverse Detection
Wenrui Zhang, Shijia Geng, Shenda Hong
Learning representations from electrocardiogram (ECG) signals can serve as a fundamental step for different machine learning-based ECG tasks. In order to extract general ECG repres…
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Ling Yang, Shenda Hong
Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations. Existing works mainly adopt the fr…
Spectral Propagation Graph Network for Few-shot Time Series Classification
Ling Yang, Shenda Hong, Luxia Zhang
Few-shot Time Series Classification (few-shot TSC) is a challenging problem in time series analysis. It is more difficult to classify when time series of the same class are not com…