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Deep learning (DL) models have been advancing automatic medical image analysis on various modalities, including echocardiography, by offering a comprehensive end-to-end training pipeline.
Banegas, J.R., Rodríguez-Artalejo, F.: Heart failure and instruments for measuring quality of life. Revista espanola de cardiologia 61
2008
Earlier work this paper cites.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15
2014
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds.) Proceedings of ICML. Proceedings of Machine Learning Research, vol. 37, pp. 448–456. PMLR, Lille, France (07–09 Jul 2015)
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: IEEE ICCV. pp. 618–626 (2017)
2017
Earlier work this paper cites.
Kusunose, K., Haga, A., Abe, T., Sata, M.: Utilization of artificial intelligence in echocardiography. Circulation Journal 83
2019
Earlier work this paper cites.
Leclerc, S., Smistad, E., Pedrosa, J., Østvik, A., Cervenansky, F., Espinosa, F., Espeland, T., Berg, E.A.R., Jodoin, P.M., Grenier, T., Lartizien, C., D’hooge, J., Lovstakken, L., Bernard, O.: Deep learning for segmentation using an open large-scale dataset in 2d echocardiography. IEEE Transactions on Medical Imaging 38
2019
Earlier work this paper cites.
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised contrastive learning. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in NeurIPS. vol. 33, pp. 18661–18673. Curran Associates, Inc. (2020)
2020
Earlier work this paper cites.
Leclerc, S., Smistad, E., Østvik, A., Cervenansky, F., Espinosa, F., Espeland, T., Rye Berg, E.A., Belhamissi, M., Israilov, S., Grenier, T., Lartizien, C., Jodoin, P.M., Lovstakken, L., Bernard, O.: Lu-net: A multistage attention network to improve the robustness of segmentation of left ventricular structures in 2-d echocardiography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 67
2020
Earlier work this paper cites.
McInnes, L., Healy, J., Melville, J.: Umap: Uniform manifold approximation and projection for dimension reduction (2020)
2020
Cited alongside, same era.
Ouyang, D., He, B., Ghorbani, A., Yuan, N., Ebinger, J., Langlotz, C.P., Heidenreich, P.A., Harrington, R.A., Liang, D.H., Ashley, E.A., et al.: Video-based ai for beat-to-beat assessment of cardiac function. Nature 580
2020
Cited alongside, same era.
Dai, W., Li, X., Chiu, W.H.K., Kuo, M.D., Cheng, K.T.: Adaptive contrast for image regression in computer-aided disease assessment. IEEE Transactions on Medical Imaging 41
2021
Cited alongside, same era.
Degerli, A., Zabihi, M., Kiranyaz, S., Hamid, T., Mazhar, R., Hamila, R., Gabbouj, M.: Early detection of myocardial infarction in low-quality echocardiography. IEEE Access 9
2021
Cited alongside, same era.
Savarese, G., Stolfo, D., Sinagra, G., Lund, L.H.: Heart failure with mid-range or mildly reduced ejection fraction. Nature Reviews Cardiology 19
2022
Later among the works it cites.
Thomas, S., Gilbert, A., Ben-Yosef, G.: Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. pp. 380–390. Springer (2022)
2022
Later among the works it cites.
Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., Mahdavi, S.S., Wulczyn, E., Babenko, B., Walker, M., Loh, A., Chen, P.H.C., Liu, Y., Bavishi, P., McKinney, S.M., Winkens, J., Roy, A.G., Beaver, Z., Ryan, F., Krogue, J., Etemadi, M., Telang, U., Liu, Y., Peng, L., Corrado, G.S., Webster, D.R., Fleet, D., Hinton, G., Houlsby, N., Karthikesalingam, A., Norouzi, M., Natarajan, V.: Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging. Nature Biomedical Engineering 7
2023
Later among the works it cites.
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Reynaud, H., Vlontzos, A., Hou, B., Beqiri, A., Leeson, P., Kainz, B.: Ultrasound video transformers for cardiac ejection fraction estimation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. pp. 495–505. Springer (2021)
2021
Cited alongside, same era.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: 2022 CVPR. pp. 15979–15988 (2022)
2022
Cited alongside, same era.
Li, K., Wang, Y., Gao, P., Song, G., Liu, Y., Li, H., Qiao, Y.: Uniformer: Unified transformer for efficient spatial-temporal representation learning. In: ICLR (2022)
2022
Cited alongside, same era.
Ling, H.J., Garcia, D., Bernard, O.: Reaching intra-observer variability in 2-D echocardiographic image segmentation with a simple U-Net architecture. In: IEEE International Ultrasonics Symposium (IUS). Venice, Italy (Oct 2022)
2022
Cited alongside, same era.
Muhtaseb, R., Yaqub, M.: Echocotr: Estimation of the left ventricular ejection fraction from spatiotemporal echocardiography. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. pp. 370–379. Springer (2022)
2022
Cited alongside, same era.
Saeed, M., Yaqub, M.: End-to-end myocardial infarction classification from echocardiographic scans. In: Aylward, S., Noble, J.A., Hu, Y., Lee, S.L., Baum, Z., Min, Z. (eds.) Simplifying Medical Ultrasound. pp. 54–63. Springer (2022)
2022
Cited alongside, same era.
Maani, F., Ukaye, A., Saadi, N., Saeed, N., Yaqub, M.: Unilvseg: Unified left ventricular segmentation with sparsely annotated echocardiogram videos through self-supervised temporal masking and weakly supervised training (2023)
2023
Later among the works it cites.
Nguyen, T., Nguyen, P., Tran, D., Pham, H., Nguyen, Q., Le, T., Van, H., Do, B., Tran, P., Le, V., et al.: Ensemble learning of myocardial displacements for myocardial infarction detection in echocardiography. Frontiers in Cardiovascular Medicine 10
2023
Later among the works it cites.
Wang, L., Huang, B., Zhao, Z., Tong, Z., He, Y., Wang, Y., Wang, Y., Qiao, Y.: Videomae v2: Scaling video masked autoencoders with dual masking. In: Proceedings of the IEEE/CVF Conference on CVPR. pp. 14549–14560 (June 2023)
2023
Later among the works it cites.
Wei, H., Ma, J., Zhou, Y., Xue, W., Ni, D.: Co-learning of appearance and shape for precise ejection fraction estimation from echocardiographic sequences. Medical Image Analysis 84
2023
Later among the works it cites.
Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. In: Conference on NeurIPS (2023)
2023
Later among the works it cites.
Sanjeev, S., Al Khatib, S.K., Shaaban, M.A., Almakky, I., Papineni, V.R., Yaqub, M.: Pecon: Contrastive pretraining to enhance feature alignment between ct and ehr data for improved pulmonary embolism diagnosis. In: Machine Learning in Medical Imaging. pp. 434–443. Springer (2024)
2024
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