Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Deep Meta Learning Based Classification Problem Learning Method for Skeletal Maturity Indication

골 성숙도 판별을 위한 심층 메타 학습 기반의 분류 문제 학습 방법

  • Min, Jeong Won (Dept. of Control, Automation and Systems Eng., Graduate School, Pusan National University) ;
  • Kang, Dong Joong (Dept. of Control, Automation and Systems Eng., Graduate School, Pusan National University)
  • Received : 2018.01.15
  • Accepted : 2018.01.22
  • Published : 2018.02.28
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Abstract

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.

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References

  1. Yunju Jeong, Israfil Ansari, Jaechang Shim and Jeonghwan Lee, "A Car Plate Area Detection System Using Deep Convolution Neural Network", Journal of Korea Multimedia Society, Vol. 20, No. 8, pp. 1166-1174, August 2017 https://doi.org/10.9717/KMMS.2017.20.8.1166
  2. S. Ravi and H. Larochellem, "Optimization as a Model for Few-Shot Learning," Proceeding of International Conference on Learning Representations, Vol. 1, pp. 6, 2017.
  3. C. Finn, P. Abbeel, and S. Levine, "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks," Proceeding of International Conference on Machine Learning, Vol. 70, pp. 1126-1135, 2017.
  4. Z. Li, F. Zhou, F. Chen, and H. Li, "Meta-SGD: Learning to Learn Quickly for Few-Shot Learning," arXiv preprint arXiv:1707.09835, 2017.
  5. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based Learning Applied to Document Recognition,” Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324, 1998. https://doi.org/10.1109/5.726791
  6. A. Bastidas, Tiny ImageNet Image Classification, https://tiny-imagenet.herokuapp.com/ (accessed Jan., 12, 2018)
  7. A. Krizhevsky, Learning Multiple Layers of Features from Tiny Images, https://www.cs.toronto.edu/-kriz/cifar.html (accessed Jan., 12, 2018).
  8. S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," Proceeding of International Conference on Machine Learning, Vol. 37, pp. 448-456, 2015.
  9. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv preprint arXiv:1409.1556, 2014.