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Electrocardiograms (ECGs) are non-invasive diagnostic tools crucial for detecting cardiac arrhythmic diseases in clinical practice.
Snomed clinical terms: overview of the development process and project status
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Scp-ecg v3. 0: An enhanced standard communication protocol for computer-assisted electrocardiography
Rubel, P., Pani, D., Schloegl, A., Fayn, J., Badilini, F., Macfarlane, P. W., and Varri, A · 2016
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An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection
Liu, F., Liu, C., Zhao, L., Zhang, X., Wu, X., Xu, X., Liu, Y., Ma, C., Wei, S., He, Z., et al · 2018
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Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
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Ptb-xl, a large publicly available electrocardiography dataset
Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F. I., Samek, W., and Schaeffter, T · 2020
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Contrastive learning of medical visual representations from paired images and text
Zhang, Y., Jiang, H., Miura, Y., Manning, C. D., and Langlotz, C. P · 2020
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Optimal multi-stage arrhythmia classification approach
Zheng, J., Chu, H., Struppa, D., Zhang, J., Yacoub, S. M., El-Askary, H., Chang, A., Ehwerhemuepha, L., Abudayyeh, I., Barrett, A., et al · 2020
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Exploring simple siamese representation learning
Chen, X. and He, K · 2021
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An empirical study of training self-supervised vision transformers. in 2021 ieee
Chen, X., Xie, S., and He, K · 2021
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Time-series representation learning via temporal and contextual contrasting
Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C. K., Li, X., and Guan, C · 2021
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Gao, T., Yao, X., and Chen, D · 2021
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Domain-specific language model pretraining for biomedical natural language processing
Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., and Poon, H · 2021
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Clocs: Contrastive learning of cardiac signals across space, time, and patients
Kiyasseh, D., Zhu, T., and Clifton, D. A · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S · 2021
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Snippet policy network for multi-class varied-length ecg early classification
Huang, Y., Yen, G. G., and Tseng, V. S · 2023
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Maira-1: A specialised large multimodal model for radiology report generation
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Medcpt: Contrastive pre-trained transformers with large-scale pubmed search logs for zero-shot biomedical information retrieval
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Practical intelligent diagnostic algorithm for wearable 12-lead ecg via self-supervised learning on large-scale dataset
Lai, J., Tan, H., Wang, J., Ji, L., Guo, J., Han, B., Shi, Y., Feng, Q., and Yang, W · 2023
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ECG representation learning with multi-modal EHR data
Lalam, S. K., Kunderu, H. K., Ghosh, S., A, H. K., Awasthi, S., Prasad, A., Lopez-Jimenez, F., Attia, Z. I., Asirvatham, S., Friedman, P., Barve, R., and Babu, M · 2023
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
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Snippet policy network v2: Knee-guided neuroevolution for multi-lead ecg early classification
Huang, Y. and Yen · 2022
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Visual classification via description from large language models
Menon, S. and Vondrick, C · 2022
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Masked autoencoder-based self-supervised learning for electrocardiograms to detect left ventricular systolic dysfunction
Sawano, S., Kodera, S., Takeuchi, H., Sukeda, I., Katsushika, S., and Komuro, I · 2022
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Maefe: Masked autoencoders family of electrocardiogram for self-supervised pretraining and transfer learning
Zhang, H., Liu, W., Shi, J., Chang, S., Wang, H., He, J., and Huang, Q · 2022
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A large scale 12-lead electrocardiogram database for arrhythmia study (version 1.0. 0)
Zheng, J., Guo, H., and Chu, H · 2022
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Generative text-guided 3d vision-language pretraining for unified medical image segmentation
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Frozen language model helps ecg zero-shot learning
Li, J., Liu, C., Cheng, S., Arcucci, R., and Hong, S · 2023
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Enhancing clip with gpt-4: Harnessing visual descriptions as prompts
Maniparambil, M., Vorster, C., Molloy, D., Murphy, N., McGuinness, K., and O’Connor, N. E · 2023
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What does a platypus look like? generating customized prompts for zero-shot image classification
Pratt, S., Covert, I., Liu, R., and Farhadi, A · 2023
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Med-halt: Medical domain hallucination test for large language models
Umapathi, L. K., Pal, A., and Sankarasubbu, M · 2023
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Med-unic: Unifying cross-lingual medical vision-language pre-training by diminishing bias
Wan, Z., Liu, C., Zhang, M., Fu, J., Wang, B., Cheng, S., Ma, L., Quilodrán-Casas, C., and Arcucci, R · 2023
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Adversarial spatiotemporal contrastive learning for electrocardiogram signals
Wang, N., Feng, P., Ge, Z., Zhou, Y., Zhou, B., and Wang, Z · 2023
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Self-supervised time series representation learning via cross reconstruction transformer
Zhang, W., Yang, L., Geng, S., and Hong, S · 2023
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