RL Based Unsupervised Video Summarization Framework for Ultrasound Imaging
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-16902-1_3
Reference16 articles.
1. Barjaktarevic, I., Kenny, J.É.S., Berlin, D., Cannesson, M.: The evolution of ultrasound in critical care: from procedural guidance to hemodynamic monitor. J. Ultras. Med. Off. J. Am. Inst. Ultras. Med. 40(2), 401 (2021)
2. Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Scale-space autoencoders for unsupervised anomaly segmentation in Brain MRI. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 552–561. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_54
3. Cao, J., Li, Y., Zhang, K., Van Gool, L.: Video super-resolution transformer. arXiv preprint arXiv:2106.06847 (2021)
4. Chen, M., Shi, X., Zhang, Y., Wu, D., Guizani, M.: Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Trans. Big Data 7(4), 750–758 (2017)
5. Gao, J., Yang, X., Zhang, Y., Xu, C.: Unsupervised video summarization via relation-aware assignment learning. IEEE Trans. Multim. 23, 3203–3214 (2020)
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