[Applications] 12. Image Generation Technique and Its Application to Medicine
Author:
Affiliation:
1. Faculty of Information Engineering, Meijo University
Publisher
Japanese Society of Radiological Technology
Link
https://www.jstage.jst.go.jp/article/jjrt/80/7/80_2024-2383/_pdf
Reference11 articles.
1. 1) Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Advances in Neural Information Processing Systems (NIPS) 2014; 2672–2680.
2. 2) Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems (NIPS) 2020; 6840–6851.
3. 3) Onishi Y, Teramoto A, Tsujimoto M, et al. Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks. Int J Comput Assist Radiol Surg 2020; 15(1): 173–178.
4. 4) Onishi Y, Teramoto A, Tsujimoto M, et al. Investigation of pulmonary nodule classification using multi-scale residual network enhanced with 3DGAN-synthesized volumes. Radiol Phys Technol 2020; 13(2): 160–169.
5. 5) Toda R, Teramoto A, Tsujimoto M, et al. Synthetic CT image generation of shape-controlled lung cancer using semi-conditional InfoGAN and its applicability for type classification. Int J Comput Assist Radiol Surg 2021; 16(2): 241–251.
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