Tu.P1.D-9
A Lightweight Multimodal Cardiovascular Disease Detection Architecture via Heterogeneous CNN-Mamba
Yongqiang Zhao, Ziwei Wang, Pengfei Lu, Shihezi University
Session:
Tu.P1: ECG Modeling, Arrhythmia, and Cardiac AI Poster
Track:
Biomedical Signal Processing
Location:
Hall E; Bay D, Board #9
Presentation Time:
Tuesday, 28 July, 16:30 - 18:30
Presentation
Discussion
Resources
No resources available.
Session Tu.P1
Tu.P1.D-1: PATIENT-STRATIFIED UNCERTAINTY QUANTIFICATION FOR ROBUST DEEP LEARNING IN AMBULATORY ECG
Ryan Yi Sheng Neo, Independent Researcher; Caden Yi Kang Neo, NUS High School of Mathematics and Science
Tu.P1.D-2: LATENT DIFFUSION WITH MULTI-CLASSIFIER GUIDANCE FOR CONDITIONAL ECG GENERATION
Yongjun Yoo, Hojun Na, Seoul National University
Tu.P1.D-3: IMPACT OF LEAD PERMUTATION ON DEEP LEARNING MODELS FOR ATRIAL FIBRILLATION DETECTION
Beatriz Cosculluela-Arasanz, Cardio Calm srl; Massimo Hu, Università degli Studi di Milano; Martino Vaglio, AMPS llc; Roberto Sassi, Massimo W. Rivolta, Università degli Studi di Milano; Fabio Badilini, AMPS llc
Tu.P1.D-4: INTRAPROCEDURAL ECG PREDICTS 1-YEAR MORTALITY IN ANTERIOR STEMI UNDERGOING REVASCULARIZATION: A PILOT AI STUDY
Zahra Nasresfahani, Seyed Reza Razavi, University of Manitoba; Ashish Shah, University of Manitoba and St. Boniface Hospital; Zahra Moussavi, University of Manitoba
Tu.P1.D-5: Anatomy-Aware Multi-Branch Autoencoder for Myocardial Infarction Detection Using 12-Lead ECG
Yerim Huh, Moogyeom Kim, The Catholic University of Korea; Chae-Bin Song, Seers Technology., CO. Ltd.; Roneel V. Sharan, University of Essex; Yun Kwan Kim, Seers Technology., CO. Ltd.; Minji Lee, The Catholic University of Korea
Tu.P1.D-6: MULTI-TASK EEG-BASED PREDICTION OF MOTOR AND COGNITIVE FUNCTIONS IN OLDER ADULTS
Jimin Jung, Yerim Huh, The Catholic University of Korea; Woohee Han, Hyunjin Kim, Jungsoo Lee, Kumoh National Institute of Technology; Minji Lee, The Catholic University of Korea
Tu.P1.D-7: MAMBA-AUGMENTED ENCODER–DECODER NEURAL NETWORKS FOR ECG DENOISING
Basile Morel, Samuel Ruiperez-Campillo, Andreas P. Streich, Julia E. Vogt, Thomas Hofmann, ETH Zurich
Tu.P1.D-8: INTERPRETING AI ECG MODELS VIA PHYSIOLOGY-INSPIRED COUNTERFACTUAL EXPLANATIONS
Eray Mutlu, Danyu Shen, Technical University of Denmark; Philip Hempel, University Medical Center Göttingen; Andreas Brink-Kjær, Lei You, Nicolai Spicher, Technical University of Denmark
Tu.P1.D-9: A Lightweight Multimodal Cardiovascular Disease Detection Architecture via Heterogeneous CNN-Mamba
Yongqiang Zhao, Ziwei Wang, Pengfei Lu, Shihezi University
Tu.P1.D-10: Deep-Learning-Based Detection of Common Localizations of End-Diastole Using Impella Derived Signals
Bálint Tóth, Maryam Alsharqi, Elazer R. Edelman, Massachusetts Institute of Technology
Tu.P1.D-11: SRI-NET: A LIGHTWEIGHT ENCODER DECODER ARCHITECTURE WITH MOBILENET ENHANCED UNET FOR REAL-TIME IRIS SEGMENTATION
Srichakri Vojjala, Arya Choyichivayalil, Rohini Palanisamy, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram
Tu.P1.D-12: Benchmarking Loss Functions for 12-Lead ECG Reconstruction from Limited Leads: A Multi-Objective Framework for Morphological Fidelity
Mithun Manivannan, Sunnybrook Health Sciences; Alex Mariakakis, University of Toronto; Christopher Cheung, Sunnybrook Health Sciences
Tu.P1.D-13: Multi-Scale Hybrid Transformer for Detecting Transient Myocardial Ischemia Using 10-Second 12-Lead STAFF III ECGs
Junmo An, Richard Gregg, HyeonWoo Lee, Dillon Dzikowicz, Ben Bailey, Philips
Tu.P1.D-14: Personalized Cardiac Digital Twin Framework for Ventricular Arrhythmia Risk Assessment
Hairui Wang, Kaihao Gu, Yanqi Huang, Danyue Mao, Xiaoqing Tang, Shengjie Yan, Xiaomei Wu, Fudan University
Tu.P1.D-15: INTERPRETABLE ECG COMPRESSION VIA GAUSSIAN MODELING
Ahmad Mousa, Khalid Elgazzar, Ontario Tech University
Tu.P1.D-16: Standardizing 12-Lead ECG Free-Text Reports to SNOMED-CT Using Mid-Sized Language Models
HyeonWoo Lee, Dillon Dzikowicz, Junmo An, Philips
Tu.P1.D-17: Class Imbalanced ECG Classification Using GAN-Based Data Augmentation and Multimodal Fusion Learning
Chika Sugimoto, Emiri Yuge, Yokohama National University
Tu.P1.D-18: Non-Linear HRV Features for the Detection of Supraventricular Arrhythmia
Ifeanyi Oguamanam, Arion Frakulli, Toronto Metropolitan University
Tu.P1.D-19: WAVELET-BASED ECG SIGNAL QUALITY INDEX VALIDATED WITH ANNOTATED WEARABLE RECORDINGS
Valerie Rennoll, Ian McLane, Perin Health Devices
Tu.P1.D-20: DEEP LEARNING-BASED QT INTERVAL EXTRACTION FROM RASTERIZED SMARTWATCH ECG EXPORTS
Arnob Haque, John Wilson, Texas A&M School of Engineering Medicine
Tu.P1.D-21: Electrode-Aware Message Passing for Flexible-Lead ECG Analysis
Rayan Ansari, Mohamed Musa, Sabyasachi Bandyopadhyay, A.J. Rogers, Stanford University
Tu.P1.D-22: CRAN: A Chunked Residual Attention Network for ECG Anomaly Classification
Sudhanshu Gaurhar, Indian Institute of Technology Jodhpur; Surender Deora, All India Institute of Medical Sciences Jodhpur; Anil Kumar Tiwari, Indian Institute of Technology Jodhpur
Tu.P1.D-23: Beyond Echocardiography: Multimodal Deep Learning for Ejection Fraction Estimation from Standard ECG
Sudhanshu Gaurhar, Indian Institute of Technology Jodhpur; Gurunath Parale, Ashwini Rural Medical College; Tushar Shinde, Indian Institute of Technology Madras,Zanzibar; Surender Deora, All India Institute of Medical Sciences Jodhpur; Anil Kumar Tiwari, Indian Institute of Technology Jodhpur
Tu.P1.D-24: Activity Aware Signal Classification and Adaptive Denoising Algorithm for ECG Monitoring Applications
Reshma Karunanithi, Srinivasa Venkatan L N, Pandiyarasan Veluswamy, Rohini Palanisamy, Indian Institute of Information Technology, Design and Manufacturing (IIITDM), Kancheepuram
Resources
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