Abstract
This paper presents experimental results obtained from using weakly tuned deep learning models as feature extraction mechanisms which are used to train regressor models for skeletal age estimation from hand radiographs of the RSNA Bone Age dataset. By leveraging transfer learning, deep learning models were initialised with the ImageNet dataset weights and then tuned for 5 epochs on the target RSNA Bone Age dataset. Thereafter, the deep learned models were used to extract features from the dataset to train various regressor models. The DenseNet201 combined with a Bayesian Ridge classifier obtained a MAE of 9.38. With the exploration of ensemble boosting and stacking, the performance improved to 8.66 months. These results suggest that weakly tuned deep learning models can be successfully used as feature extraction mechanisms with the advantage of not having to train the deep learning model excessively.
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References
Budka, M., Gabrys, B.: Density-preserving sampling: robust and efficient alternative to cross-validation for error estimation. IEEE Trans. Neural Netw. Learn. Syst. 24(1), 22–34 (2013)
Castillo, J.C., Tong, Y., Zhao, J., Zhu, F.: RSNA bone-age detection using transfer learning and attention mapping, p. 5 (2018)
Iglovikov, V., Rakhlin, A., Kalinin, A., Shvets, A.: Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks. arXiv:1712.05053 [cs], 11045:300–308 (2018)
Kattukaran, M.S., Abraham, A.: Bone age assessment using deep. Learning 9(2), 4 (2018)
Pietka, E., Gertych, A., Pospiech, S., Cao, F., Huang, H.K., Gilsanz, V.: Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction. IEEE Trans. Med. Imaging 20(8), 715–729 (2001)
Poznanski, A.K., Garn, S.M., Nagy, J.M., Gall, J.C.: Metacarpophalangeal pattern profiles in the evaluation of skeletal malformations. Radiology 104(1), 1–11 (1972)
Rakhlin, A: Diabetic Retinopathy detection through integration of Deep Learning classification framework. bioRxiv, June 2018
Rakhlin, A., Shvets, A., Iglovikov, V., Kalinin, A.: Deep convolutional neural networks for breast cancer histology image analysis. bioRxiv, April 2018
Souza, D., Oliveira, M.M.: End-to-end bone age assessment with residual learning. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, pp. 197–203. IEEE, October 2018
Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., Saarakkala, S.: Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci. Rep. 8(1) (2018)
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Hirasen, D., Pillay, V., Viriri, S., Gwetu, M. (2021). Skeletal Age Estimation from Hand Radiographs Using Ensemble Deep Learning. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_17
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DOI: https://doi.org/10.1007/978-3-030-77004-4_17
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