Skip to main content
main-content
Top

Hint

Swipe to navigate through the chapters of this book

2019 | OriginalPaper | Chapter

Multi-layer Domain Adaptation for Deep Convolutional Networks

share
SHARE

Abstract

Despite their success in many computer vision tasks, convolutional networks tend to require large amounts of labeled data to achieve generalization. Furthermore, the performance is not guaranteed on a sample from an unseen domain at test time, if the network was not exposed to similar samples from that domain at training time. This hinders the adoption of these techniques in clinical setting where the imaging data is scarce, and where the intra- and inter-domain variance of the data can be substantial. We propose a domain adaptation technique that is especially suitable for deep networks to alleviate this requirement of labeled data. Our method utilizes gradient reversal layers [4] and Squeeze-and-Excite modules [6] to stabilize the training in deep networks. The proposed method was applied to publicly available histopathology and chest X-ray databases and achieved superior performance to existing state-of-the-art networks with and without domain adaptation. Depending on the application, our method can improve multi-class classification accuracy by 5–20% compared to DANN introduced in [4].
Literature
1.
go back to reference Aresta, G., et al.: Bach: grand challenge on breast cancer histology images. Med. Image Anal. 56, 122–139 (2019) CrossRef Aresta, G., et al.: Bach: grand challenge on breast cancer histology images. Med. Image Anal. 56, 122–139 (2019) CrossRef
5.
go back to reference Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in neural information processing systems, pp. 5767–5777 (2017) Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in neural information processing systems, pp. 5767–5777 (2017)
6.
go back to reference Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141 (2018) Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141 (2018)
10.
go back to reference Saito, K., Ushiku, Y., Harada, T.: Asymmetric tri-training for unsupervised domain adaptation. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2988–2997 (2017). JMLR.​org Saito, K., Ushiku, Y., Harada, T.: Asymmetric tri-training for unsupervised domain adaptation. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2988–2997 (2017). JMLR.​org
11.
go back to reference Shimodaira, H.: Improving predictive inference under covariate shift by weighting the log-likelihood function. J. Stat. Plann. Infer. 90(2), 227–244 (2000) MathSciNetCrossRef Shimodaira, H.: Improving predictive inference under covariate shift by weighting the log-likelihood function. J. Stat. Plann. Infer. 90(2), 227–244 (2000) MathSciNetCrossRef
Metadata
Title
Multi-layer Domain Adaptation for Deep Convolutional Networks
Authors
Ozan Ciga
Jianan Chen
Anne Martel
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_3

Premium Partner