References
- M. JAŠČUR, M. BUNDZEL, M. MALÍK, A. DZIAN, N. FERENČÍK, and F. BABIČ, “Detecting the absence of lung sliding in lung ultrasounds using deep learning,” Applied Sciences, vol. 11, no. 15, p. 6976, 2021.
- M. L. GIGER, “Machine learning in medical imaging,” Journal of the American College of Radiology, vol. 15, no. 3, pp. 512–520, 2018.
- D. SUN, S. ROTH, J. P. LEWIS, and M. J. BLACK, “Learning optical flow,” in European Conference on Computer Vision, pp. 83–97, Springer, 2008.
- F. HE, T. LIU, and D. TAO, “Why resnet works? residuals generalize,” IEEE transactions on neural networks and learning systems, vol. 31, no. 12, pp. 5349–5362, 2020.
- Z. LI, F. LIU, W. YANG, S. PENG, and J. ZHOU, “A survey of convolutional neural networks: analysis, applications, and prospects,” IEEE transactions on neural networks and learning systems, vol. 33, no. 12, pp. 6999–7019, 2021.
- M. GAO, P. SONG, F. WANG, J. LIU, A. MANDELIS, and D. QI, “A novel deep convolutional neural network based on resnet-18 and transfer learning for detection of wood knot defects,” Journal of Sensors, vol. 2021, no. 1, p. 4428964, 2021.
- J. WU, “Introduction to convolutional neural networks,” National Key Lab for Novel Software Technology. Nanjing University. China, vol. 5, no. 23, p. 495, 2017.
- S. E. EBADI, D. KRISHNASWAMY, S. E. S. BOLOURI, D. ZONOOBI, R. GREINER, N. MEUSER-HERR, J. L. JAREMKO, J. KAPUR, M. NOGA, and K. PUNITHAKUMAR, “Automated detection of pneumonia in lung ultrasound using deep video classification for covid-19,” Informatics in medicine unlocked, vol. 25, p. 100687, 2021.
- M. KOLARIK, M. SARNOVSKÝ, and J. PARALIČ, “Detecting the absence of lung sliding in ultrasound videos using 3d convolutional neural networks,” Acta Polytechnica Hungarica, vol. 20, no. 6, pp. 47–60, 2023.
- S. ROY, W. MENAPACE, S. OEI, B. LUIJTEN, E. FINI, C. SALTORI, I. HUIJBEN, N. CHENNAKESHAVA, F. MENTO, A. SENTELLI, et al., “Deep learning for classification and localization of covid-19 markers in point-of-care lung ultrasound,” IEEE transactions on medical imaging, vol. 39, no. 8, pp. 2676–2687, 2020.
- A. HUANG, L. JIANG, J. ZHANG, and Q. WANG, “Attention-vgg16-unet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images,” Quantitative imaging in medicine and surgery, vol. 12, no. 6, p. 3138, 2022.
- K. ARDON-DRYER and A. TAIRU, “Impacts of african dust storm particles on human lung epithelial cells,” in American Meteorological Society Meeting Abstracts, vol. 101, p. 31, 2021.
- S. S. BEAUCHEMIN and J. L. BARRON, “The computation of optical flow,” ACM computing surveys (CSUR), vol. 27, no. 3, pp. 433–466, 1995.
- A. BRUHN, J. WEICKERT, and C. SCHNÖRR, “Lucas/kanade meets horn/schunck: Combining local and global optic flow methods,” International journal of computer vision, vol. 61, pp. 211–231, 2005.
- Z. TEED and J. DENG, “Raft: Recurrent all-pairs field transforms for optical flow,” in Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23 – 28, 2020, Proceedings, Part II 16, pp. 402–419, Springer, 2020.
- Z. TEED and J. DENG, “Raft: Recurrent all-pairs field transforms for optical flow,” in Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23 – 28, 2020, Proceedings, Part II 16, pp. 402–419, Springer, 2020.
- G. FERNEBÄCK, “Two-frame motion estimation based on polynomial expansion,” in Image Analysis: 13th Scandinavian Conference, SCIA 2003 Halmstad, Sweden, June 29 – July 2, 2003 Proceedings 13, pp. 363–370, Springer, 2003.
- S. MALLICK, “Optical flow using deep learning: Raft,” 2023. Accessed: 2024-05-14.
- G. MASON-WILLIAMS and F. DAHLQVIST, “What makes a good prune? maximal unstructured pruning for maximal cosine similarity,” in The Twelfth International Conference on Learning Representations, 2024.
