The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Understanding the Mechanisms of Deep Transfer Learning for Medical Images
Open accessAbstract
The ability to automatically learn task specific feature representations has led to a huge success of deep learning methods. When large training data is scarce, such as in medical imaging problems, transfer learning has been very effective. In this paper, we systematically investigate the process of transferring a Convolutional Neural Network, trained on ImageNet images to perform image classification, to kidney detection problem in ultrasound images. We study how the detection performance depends on the extent of transfer. We show that a transferred and tuned CNN can outperform a state-of-the-art feature engineered pipeline and a hybridization of these two techniques achieves 20 % higher performance. We also investigate how the evolution of intermediate response images from our network. Finally, we compare these responses to state-of-the-art image processing filters in order to gain greater insight into how transfer learning is able to effectively manage widely varying imaging regimes.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
Cited by
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
Hesamian, M. H., Jia, W., He, X., & Kennedy, P. (2019) · Journal of Digital Imaging · doi:10.1007/s10278-019-00227-x · Cited by 1,631
Automated breast ultrasound lesions detection using convolutional neural networks
Yap, M. H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A. K., & Martí, R. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2731873 · Cited by 1,086
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
Horry, M. J., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., & Shukla, N. (2020) · IEEE Access · doi:10.1109/access.2020.3016780 · Cited by 472
Huang, Z., Pan, Z., & Lei, B. (2017) · Remote Sensing · doi:10.3390/rs9090907 · Cited by 437
Metadata
- Authors
- Ravishankar, H., Sudhakar, P., Venkataramani, R., Thiruvenkadam, S., Annangi, P., Babu, N., & Vaidya, V.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 188-196
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2521537533
- Cited by
- 189
- References
- 3
- FWCI
- 9.26
- Type
- conference paper
- Language
- en
- Open access
- green
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- Ravishankar, H., Sudhakar, P., Venkataramani, R., Thiruvenkadam, S., Annangi, P., Babu, N., & Vaidya, V. (2016). Understanding the Mechanisms of Deep Transfer Learning for Medical Images. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-46976-8_20
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