Artificial intelligence in computer-aided diagnosis of abdomen diseases

Author:

Gao Fei,Zhu Yi,Zhang Jue

Publisher

Springer Science and Business Media LLC

Subject

General Agricultural and Biological Sciences,General Environmental Science,General Biochemistry, Genetics and Molecular Biology

Reference19 articles.

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2. Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., and Greenspan, H. (2016). Fully convolutional network for liver segmentation and lesions detection. In Deep Learning and Data Labeling for Medical Applications. G. Carneiro, D. Mateus, L. Peter, A. Bradley, J.M.R. Tavares, V. Belagiannis, J.P. Papa, J.C. Nascimento, M. Loog, Z. Lu, J.S. Cardoso, and J. Cornebise, ed. (Athens, Greece), pp. 77–85.

3. Christ, P.F., Elshaer, M.E.A., Ettlinger, F., Tatavarty, S., Bickel, M., Bilic, P., Rempfler, M., Armbruster, M., Hofmann, F., D’Anastasi, M., et al. (2016). Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention, S. Ourselin, L. Joskowicz, M.R. Sabuncu, G. Unal, and W. Wells, ed. (Athens, Greece), pp. 415–423.

4. Dhawan, A.P. (2011). Medical Image Analysis (Hoboken: John Wiley & Sons).

5. Gao, G., Wang, C., Zhang, X., Hu, J., Yang, X., Wang, H., Zhang, J., and Wang, X. (2017). Quantitative analysis of diffusion-weighted magnetic resonance images: differentiation between prostate cancer and normal tissue based on a computer-aided diagnosis system. Sci China Life Sci 60, 37–43.

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