Panzer, W., P. Shrimpton, Jessen K.: European Guidelines on Quality Criteria for Computed Tomography. Office for Official Publications of the European Communities, 2020
Foos, D. H., Sehnert, W. J., Reiner, B., Siegel, E. L., Segal, A., Waldman, D. L.: Digital radiography reject analysis: data collection methodology, results, and recommendations from an in-depth investigation at two hospitals. Journal of digital imaging: 22, 89–98, 2009
Bushberg, J. T., Boone J. M.: The essential physics of medical imaging, Lippincott Williams & Wilkins, 2011
Fang, X., Harris, L., Zhou, W., and Huo, D., Generalized radiographic view identification with deep learning. Journal of Digital Imaging: 34(1), 66–74, 2021
Mairhöfer, D., Laufer, M., Simon, P. M., Sieren, M., Bischof, A., Käster, T., Barth E., Barkhausen J., Martinetz, T.: An AI-based framework for diagnostic quality assessment of ankle radiographs. International Conference on Medical Imaging with Deep Learning, 2021
Simonyan, K., Zisserman A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition: 770–778, 2016
Kim, T. K., Yi, P. H., Wei, J., Shin, J. W., Hager, G., Hui, F. K., Sair, H. I., Lin, C. T.: Deep learning method for automated classification of anteroposterior and posteroanterior chest radiographs. Journal of digital imaging: 32, 925–930, 2019
Hosch, R., Kroll, L., Nensa, F., Koitka, S.: Differentiation between anteroposterior and posteroanterior chest X-ray view position with convolutional neural networks. In RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren: 193(2), 168–176, 2021
Saun, T.J.: Automated classification of radiographic positioning of hand X-rays using a deep neural network. Plastic Surgery: 29(2), 75–80, 2021
Article PubMed PubMed Central Google Scholar
Wang, C. Y., Yeh, I. H., Liao, H. Y. M.: Yolov9: Learning what you want to learn using programmable gradient information. arXiv preprint arXiv:2402.13616, 2024
Medaramatla, S. C., Samhitha, C. V., Pande, S. D., Vinta, S. R.: Detection of Hand Bone Fractures in X-ray Images using Hybrid YOLO NAS. IEEE Accessed 2024
Zheng, C., Wu, W., Chen, C., Yang, T., Zhu, S., Shen, J., Kehtarnavaz N., Shah, M.: Deep learning-based human pose estimation: A survey. ACM Computing Surveys: 56(1), 1–37, 2023
Kadkhodamohammadi, A., Gangi, A., de Mathelin, M., Padoy, N.: Articulated clinician detection using 3D pictorial structures on RGB-D data. Medical image analysis: 35, 215–224, 2017
Srivastav V., Gangi A., and Padoy N.: Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room. Medical Image Analysis: 80, 102525, 2022
Bigalke, A., Hansen, L., Diesel, J., Hennigs, C., Rostalski, P., Heinrich, M. P.: Anatomy-guided domain adaptation for 3D in-bed human pose estimation. Medical Image Analysis: 89, 102887, 2023
Ni, H., Xue, Y., Ma, L., Zhang, Q., Li, X., Huang, S. X.: Semi-supervised body parsing and pose estimation for enhancing infant general movement assessment. Medical Image Analysis: 83, 102654, 2023
Ogundokun, R. O., Maskeliūnas, R., Damaševičius, R.: Human posture detection using image augmentation and hyperparameter-optimized transfer learning algorithms. Applied Sciences: 12(19), 10156, 2022
Liu, Z., Mao, H., Wu, C. Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition: 11976–11986, 2022
Wightman, R.: PyTorch Image Models. Availble at GitHub https://github.com/huggingface/pytorch-image-models. Accessed June 2024
Wright, L.: New deep learning optimizer, ranger: Synergistic combination of radam+ lookahead for the best of both. Availabl at Github https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer. Accessed Aug 2023
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition: 248–255, 2009
Smith, L. N.: Cyclical learning rates for training neural networks. In 2017 IEEE winter conference on applications of computer vision (WACV): 464–472, 2017
Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146, 2018
Paszke, A., Gross, S., Chintala S., Chanan G., Yang E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch. In proceedings of the Conference on Neural Information Processing Systems (NIPS), 2017
Howard, J., Gugger S.: Fastai: A layered API for deep learning. Information: 11(2), 108, 2020
Bradski, G.: The openCV library. Dr. Dobb's Journal: Software Tools for the Professional Programmer: 25(11), 120–123, 2000
Li, Y., Hu, J., Wen, Y., Evangelidis, G., Salahi, K., Wang, Y., Tulyakov, S., Ren, J.: Rethinking vision transformers for mobilenet size and speed. In Proceedings of the IEEE/CVF International Conference on Computer Vision:16889–16900, 2023
Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Wang, W., Banbury, C., Ye, C., Akin, B., Aggarwal, V., Zhu, T., Moro, D., Howard, A.: MobileNetV4-Universal Models for the Mobile Ecosystem. arXiv preprint arXiv:2404.10518, 2024
Bouwmans, T., Javed, S., Sultana, M., Jung, S. K.: Deep neural network concepts for background subtraction: A systematic review and comparative evaluation. Neural Networks: 117, 8–66, 2019
England, N., Improvement N.: Diagnostic imaging dataset statistical release, in London. Department of Health, 2023
Comments (0)