no code implementations • 2 Mar 2023 • Yang Liu, Yue-Jie Hou, Chen-Xin Qin, Xin-Hui Li, Si-Jing Li, Bin Wang, Chi-Chun Zhou
Result: The result of the 3DVNET model with the augmentation method for pulmonary nodule detection shows that the proposed data augmentation method outperforms the method based on generative adversarial network (GAN) framework, training accuracy improved by 1. 5%, and with embedding mechanism for pulmonary nodules classification shows that the embedding mechanism improves the accuracy and robustness for the classification of pulmonary nodules obviously, the model training accuracy is close to 1 and the model testing F1-score is 0. 90. Conclusion:he proposed data augmentation method and embedding mechanism are beneficial to improve the accuracy and robustness of the model, and can be further applied in other common diagnostic imaging tasks.
no code implementations • 12 Jun 2022 • Hao Zhu, Wan-Jing Nie, Yue-Jie Hou, Qi-Meng Du, Si-Jing Li, Chi-Chun Zhou
In this paper, based on the convolutional auto-encoder with constraints (CCAE), an unsupervised deep-learning model proposed in the classification of the fingerprint, we propose this model for the classification of the bone age and baptize it BA-CCAE.