The Utility of Applying Various Image Preprocessing Strategies to Reduce the Ambiguity in Deep Learning-based Clinical Image Diagnosis

Author:

Tachibana Yasuhiko1,Obata Takayuki1,Kershaw Jeff1,Sakaki Hironao2,Urushihata Takuya3,Omatsu Tokuhiko1,Kishimoto Riwa1,Higashi Tatsuya4

Affiliation:

1. Applied MRI Research, Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology

2. Kansai Photon Science Institute, National Institutes for Quantum and Radiological Science and Technology

3. Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology

4. Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology

Publisher

Japanese Society for Magnetic Resonance in Medicine

Subject

Radiology, Nuclear Medicine and imaging

Reference25 articles.

1. 1. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42:60–88.

2. 2. Perone CS, Calabrese E, Cohen-Adad J. Spinal cord gray matter segmentation using deep dilated convolutions. 2017; arXiv:1710.01269v1. doi.org/10.1038/s41598-018-24304-3

3. 3. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012; 1:1097–1105.

4. 4. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc. IEEE 1998; 86:2278–2324.

5. 5. Hosseini-Asl E, Gimel’farb G, El-Baz A. Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network. 2016; arXiv:1607.00556v1.

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