Suchergebnisse - Contractile Autoencoder~

The cost of explainability in artificial intelligence-enhanced electrocardiogram models.

Autoren: Patlatzoglou, Konstantinos Pastika, Libor Barker, Joseph et al.

Quelle: NPJ Digital Medicine; 12/5/2025, Vol. 8 Issue 1, p1-13, 13p

Schlagwörter: MORTALITY risk factors, DECISION support systems, RISK assessment, PEARSON correlation (Statistics), PREDICTION models, RECEIVER operating characteristic curves, RESEARCH funding, ARTIFICIAL intelligence, AUTOENCODERS, SEX distribution, AGE distribution, DESCRIPTIVE statistics, ELECTROCARDIOGRAPHY, LONGITUDINAL method, CONCEPTUAL structures, DEEP learning, DATA analysis software, REGRESSION analysis

Geografische Kategorien: MASSACHUSETTS

Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

Autoren: Sawano, Shinnosuke Kodera, Satoshi Setoguchi, Naoto et al.

Quelle: PLoS ONE; 8/14/2024, Vol. 19 Issue 8, p1-17, 17p

Schlagwörter: ARTIFICIAL neural networks, LEFT ventricular dysfunction, TRANSFORMER models, RECEIVER operating characteristic curves, UNIVERSITY hospitals, AUTOENCODERS

Deep Learning Approach for Automatic Heartbeat Classification.

Autoren: Guerra, Roger de T. Yamaguchi, Cristina K. Stefenon, Stefano F. et al.

Quelle: Sensors (14248220); Mar2025, Vol. 25 Issue 5, p1400, 25p

Schlagwörter: LONG short-term memory, DEEP learning, AUTOENCODERS, AUTOMATIC classification, PHYSICIANS, ARRHYTHMIA

Unsupervised deep learning of electrocardiograms enables scalable human disease profiling.

Autoren: Friedman, Sam F. Khurshid, Shaan Venn, Rachael A. et al.

Quelle: NPJ Digital Medicine; 1/12/2025, Vol. 8 Issue 1, p1-13, 13p

Schlagwörter: RISK assessment, METABOLIC disorders, DATABASE management, RECEIVER operating characteristic curves, AUTOENCODERS, LOGISTIC regression analysis, DESCRIPTIVE statistics, META-analysis, ELECTROCARDIOGRAPHY, DISEASES, ODDS ratio, DEEP learning, MEDICAL coding, LUNG diseases, DATA analysis software, CONFIDENCE intervals, ENDOCRINE diseases, PHENOTYPES, VASCULAR diseases, WAVE analysis

RNA-Binding Protein Signature in Proliferative Cardiomyocytes: A Cross-Species Meta-Analysis from Mouse, Pig, and Human Transcriptomic Profiling Data.

Autoren: Nguyen, Thanh Hao, Kaili Nakada, Yuji et al.

Quelle: Biomolecules (2218-273X); Feb2025, Vol. 15 Issue 2, p310, 21p

Schlagwörter: GENE expression, RNA-binding proteins, HEART beat, RNA sequencing, AUTOENCODERS, MYOCARDIAL reperfusion

Identification of intraoperative hypotension endotypes and revolution with a temporal deep learning algorithm.

Autoren: Zhu, Yihao Hao, Xuechao Li, Peiyi et al.

Quelle: Perioperative Medicine; 10/14/2025, Vol. 14 Issue 1, p1-13, 13p

Schlagwörter: HYPOTENSION, HEMODYNAMICS, VASCULAR resistance, DEEP learning, K-means clustering, CARDIAC output

Detecting Left Ventricular Systolic Dysfunction in Left Bundle Branch Block Patients Using Electrocardiogram: A Deep Learning Approach with Limited Data.

Autoren: Kwon, Chanjin Gwag, Hye Bin Seok, Jongwon

Quelle: Applied Sciences (2076-3417); Aug2025, Vol. 15 Issue 15, p8384, 14p

Schlagwörter: LEFT ventricular dysfunction, BUNDLE-branch block, CONVOLUTIONAL neural networks, OUTLIER detection, SAMPLE size (Statistics), ARTIFICIAL intelligence, DEEP learning, ELECTROCARDIOGRAPHY

Multimodal pretraining for unsupervised protein representation learning.

Autoren: Nguyen, Viet Thanh Duy Hy, Truong Son

Quelle: Biology Methods & Protocols; 2024, Vol. 9 Issue 1, p1-15, 15p

Schlagwörter: LANGUAGE models, TERTIARY structure, AUTOENCODERS, DEEP learning, DRUG design

Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition.

Autoren: Sabouri, Maziar Hajianfar, Ghasem Hosseini, Zahra et al.

Quelle: Journal of Digital Imaging; Apr2023, Vol. 36 Issue 2, p497-509, 13p, 2 Diagrams, 6 Charts, 2 Graphs

Schlagwörter: ECHOCARDIOGRAPHY, MACHINE learning, CARDIAC contraction, RETROSPECTIVE studies, CARDIAC pacing, RADIOMICS, SINGLE-photon emission computed tomography, ELECTROCARDIOGRAPHY, RESEARCH funding, PERFUSION imaging, LOGISTIC regression analysis, PERFUSION, ALGORITHMS

SpaCross deciphers spatial structures and corrects batch effects in multi-slice spatially resolved transcriptomics.

Autoren: Fang, Donghai Min, Wenwen

Quelle: Communications Biology; 9/30/2025, Vol. 8 Issue 1, p1-19, 19p

Schlagwörter: GENE expression, TRANSCRIPTOMES, PATTERN recognition systems, GRAPH neural networks, MORPHOLOGY

Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram.

Autoren: Sieliwonczyk, Ewa Sau, Arunashis Patlatzoglou, Konstantinos et al.

Quelle: Genome Medicine; 10/9/2025, Vol. 17 Issue 1, p1-17, 17p

Schlagwörter: ELECTROCARDIOGRAPHY, GENOME-wide association studies, LATENT variables, FEATURE extraction, GENETIC markers, ELECTROPHYSIOLOGY, DEEP learning

Frame Rate Up-Conversion in Echocardiography Using a Conditioned Variational Autoencoder and Generative Adversarial Model

Autoren: Teresa Tsang Purang Abolmaesumi Ken Gin et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783030322441

Zugangs-URL: https://rd.springer.com/chapter/10.1007/978-3-030-32245-8_78
https://link.springer.com/chapter/10.1007%2F978-3-030-32245-8_78
https://dblp.uni-trier.de/db/conf/miccai/miccai2019-2.html#DezakiGRGAT19

Cascaded Triplanar Autoencoder M-Net for Fully Automatic Segmentation of Left Ventricle Myocardial Scar From Three-Dimensional Late Gadolinium-Enhanced MR Images.

Autoren: Lin, Mingquan Jiang, Mingjie Zhao, Mingbo et al.

Quelle: IEEE Journal of Biomedical & Health Informatics; Jun2022, Vol. 26 Issue 6, p2582-2593, 12p

Schlagwörter: MAGNETIC resonance imaging, CONVOLUTIONAL neural networks, SCARS, IMAGE analysis, MAGNETIC resonance, HYPERTROPHIC scars

Enhancing Chicago Classification diagnoses with functional lumen imaging probe—mechanics (FLIP‐MECH).

Autoren: Halder, Sourav Yamasaki, Jun Liu, Xinyi et al.

Quelle: Neurogastroenterology & Motility; Aug2024, Vol. 36 Issue 8, p1-15, 15p

Schlagwörter: ESOPHAGEAL motility disorders, FISHER discriminant analysis, ESOPHAGOGASTRIC junction, DIAGNOSIS, GENERATIVE artificial intelligence, DEEP learning

VAE-MOTION: A deep generative model for cardiomyocyte contractility analysis for improving drug efficacy evaluation

Autoren: Curci, Giorgia Casti, Paola Sala, Luca et al.

Weitere Verfasser: Curci, Giorgia Casti, Paola Sala, Luca et al.

Schlagwörter: Variational autoencoders, Time-lapse microscopy, Contraction analysis, Data restoration

Dateibeschreibung: ELETTRONICO

Relation: volume:4; firstpage:1; lastpage:24; numberofpages:24; journal:EXPERT SYSTEMS WITH APPLICATIONS; https://hdl.handle.net/11585/1027857

Verfügbarkeit: https://hdl.handle.net/11585/1027857
https://doi.org/10.1016/j.eswa.2025.130302

Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder.

Autoren: Jang, Jong-Hwan Kim, Tae Young Lim, Hong-Seok et al.

Quelle: PLoS ONE; 12/1/2021, Vol. 16 Issue 12, p1-16, 16p

Schlagwörter: INTENSIVE care units, ELECTROCARDIOGRAPHY, SPACE exploration, DEEP learning, BRUGADA syndrome

Cardiomyocyte Cell-Cycle Regulation in Neonatal Large Mammals: Single Nucleus RNA-Sequencing Data Analysis via an Artificial-Intelligence–Based Pipeline

Autoren: Thanh Nguyen Yuhua Wei Yuji Nakada et al.

Quelle: Frontiers in Bioengineering and Biotechnology, Vol 10 (2022)

Schlagwörter: single-nucleus RNA-sequencing, heart, infarct, cardiomyocytes, cell cycle, autoencoder, Biotechnology, TP248.13-248.65

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fbioe.2022.914450/full; https://doaj.org/toc/2296-4185

Zugangs-URL: https://doaj.org/article/8b7ff3f997064591aa27f5e09b6959d3

Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder.

Autoren: Chen, Lili Hao, Yaru Hu, Xue

Quelle: PLoS ONE; 4/16/2019, Vol. 14 Issue 4, p1-16, 16p

Schlagwörter: PREMATURE labor, WAVELET transforms, SUPPORT vector machines, TIME series analysis, MACHINE learning

Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.

Autoren: Donmazov, Samir Saruhan, Eda Nur Pekkan, Kerem et al.

Quelle: Cardiovascular Engineering & Technology; Oct2024, Vol. 15 Issue 5, p522-549, 28p

Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies.

Autoren: Papadopoulos, Dimitris Karalis, Vangelis D.

Quelle: Applied Sciences (2076-3417); Apr2024, Vol. 14 Issue 7, p2970, 15p

Schlagwörter: ARTIFICIAL neural networks, GENERATIVE artificial intelligence, SAMPLE size (Statistics), DEEP learning, MACHINE learning, MONTE Carlo method