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The accurate interpretation of Electrocardiogram (ECG) signals is pivotal for diagnosing cardiovascular diseases.
Exploring the limits of transfer learning with a unified text-to-text transformer, 2023
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 1910
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Self-supervised co-training for video representation learning, 2021
Han, T., Xie, W., and Zisserman, A · 2010
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J · 2018
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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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Multi-task self-supervised learning for human activity detection
Saeed, A., Ozcelebi, T., and Lukkien, J · 2019
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Fusing transformer model with temporal features for ecg heartbeat classification
Yan, G., Liang, S., Zhang, Y., and Liu, F · 2019
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wav2vec 2.0: A framework for self-supervised learning of speech representations
Baevski, A., Zhou, Y., Mohamed, A., and Auli, M · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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A review on deep learning methods for ecg arrhythmia classification
Ebrahimi, Z., Loni, M., Daneshtalab, M., and Gharehbaghi, A · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al · 2020
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Deberta: Decoding-enhanced bert with disentangled attention
He, P., Liu, X., Gao, J., and Chen, W · 2020
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Automatic diagnosis of the 12-lead ECG using a deep neural network
Ribeiro, A. H., Ribeiro, M. H., Paixão, G. M. M., Oliveira, D. M., Gomes, P. R., Canazart, J. A., Ferreira, M. P. S., Andersson, C. R., Macfarlane, P. W., Meira Jr., W., Schön, T. B., and Ribeiro, A. L. P · 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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Exploring simple siamese representation learning
Chen, X. and He, K · 2021
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An empirical study of training self-supervised vision transformers
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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3kg: Contrastive learning of 12-lead electrocardiograms using physiologically-inspired augmentations
Gopal, B., Han, R., Raghupathi, G., Ng, A., Tison, G., and Rajpurkar, P · 2021
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Hybrid generative-contrastive representation learning, 2021
Kim, S., Kim, S., and Lee, J · 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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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
Will two do? varying dimensions in electrocardiography: the physionet/computing in cardiology challenge 2021
Reyna, M. A., Sadr, N., Alday, E. A. P., Gu, A., Shah, A. J., Robichaux, C., Rad, A. B., Elola, A., Seyedi, S., Ansari, S., et al · 2021
Ecg representation learning with multi-modal ehr data
Lalam, S. K., Kunderu, H. K., Ghosh, S., Kumar, H., Awasthi, S., Prasad, A., Lopez-Jimenez, F., Attia, Z. I., Asirvatham, S., Friedman, P., et al · 2023
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Fine-tuned clip models are efficient video learners, 2023
Rasheed, H., Khattak, M. U., Maaz, M., Khan, S., and Khan, F. S · 2023
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Attention is all you need, 2023
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 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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Sigmoid loss for language image pre-training
Zhai, X., Mustafa, B., Kolesnikov, A., and Beyer, L · 2023
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Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
Siontis, K. C., Noseworthy, P. A., Attia, Z. I., and Friedman, P. A · 2021
Cited alongside, same era.
Unsupervised representation learning for time series with temporal neighborhood coding, 2021
Tonekaboni, S., Eytan, D., and Goldenberg, A · 2021
Cited alongside, same era.
Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S · 2021
Cited alongside, same era.
Multi-modal masked autoencoders for medical vision-and-language pre-training
Chen, Z., Du, Y., Hu, J., Liu, Y., Li, G., Wan, X., and Chang, T.-H · 2022
Cited alongside, same era.
Self-knowledge distillation based self-supervised learning for covid-19 detection from chest x-ray images
Li, G., Togo, R., Ogawa, T., and Haseyama, M · 2022
Cited alongside, same era.
Lead-agnostic self-supervised learning for local and global representations of electrocardiogram
Oh, J., Chung, H., Kwon, J.-m., Hong, D.-g., and Choi, E · 2022
Cited alongside, same era.
Negative sampling for contrastive representation learning: A review
Xu, L., Lian, J., Zhao, W. X., Gong, M., Shou, L., Jiang, D., Xie, X., and Wen, J.-R · 2022
Cited alongside, same era.
Self-supervised time series representation learning via cross reconstruction transformer
Zhang, W., Yang, L., Geng, S., and Hong, S · 2023
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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 · 2023
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Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al · 2024
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The faiss library
Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., and Jégou, H · 2024
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Frozen language model helps ecg zero-shot learning
Li, J., Liu, C., Cheng, S., Arcucci, R., and Hong, S · 2024
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Multimodality helps unimodality: Cross-modal few-shot learning with multimodal models, 2024
Lin, Z., Yu, S., Kuang, Z., Pathak, D., and Ramanan, D · 2024
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Etp: Learning transferable ecg representations via ecg-text pre-training
Liu, C., Wan, Z., Cheng, S., Zhang, M., and Arcucci, R · 2024
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Ecg-fm: An open electrocardiogram foundation model
McKeen, K., Oliva, L., Masood, S., Toma, A., Rubin, B., and Wang, B · 2024
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Guiding masked representation learning to capture spatio-temporal relationship of electrocardiogram
Na, Y., Park, M., Tae, Y., and Joo, S · 2024
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Foundation models for electrocardiograms
Song, J., Jang, J.-H., Tak Lee, B., Hong, D., Kwon, J.-m., and Jo, Y.-Y · 2024
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Yu, H., Guo, P., and Sano, A · 2024
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