Cited by 253Brain Tumor Detection and ClassificationGenerative Adversarial Networks and Image SynthesisArtificial Intelligence in Healthcare and Education

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Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection

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Han, Changhee, Rundo, Leonardo, Araki, Ryosuke, Nagano, Yudai, Furukawa, Yujiro, Mauri, Giancarlo, Nakayama, Hideki, Hayashi, Hideaki·2019·IEEE Access

DOI 10.1109/access.2019.2947606

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Abstract

Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fragmented. In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting data with noise-to-image (e.g., random noise samples to diverse pathological images) or image-to-image GANs (e.g., a benign image to a malignant one). Yet, no research has reported results combining noise-to-image and image-to-image GANs for further performance boost. Therefore, to maximize the DA effect with the GAN combinations, we propose a two-step GAN-based DA that generates and refines brain Magnetic Resonance (MR) images with/without tumors separately: (i) Progressive Growing of GANs (PGGANs), multi-stage noise-to-image GAN for high-resolution MR image generation, first generates realistic/diverse 256×256 images; (ii) Multimodal UNsupervised Image-to-image Translation (MUNIT) that combines GANs/Variational AutoEncoders or SimGAN that uses a DA-focused GAN loss, further refines the texture/shape of the PGGAN-generated images similarly to the real ones. We thoroughly investigate CNN-based tumor classification results, also considering the influence of pre-training on ImageNet and discarding weird-looking GAN-generated images. The results show that, when combined with classic DA, our two-step GAN-based DA can significantly outperform the classic DA alone, in tumor detection (i.e., boosting sensitivity 93.67% to 97.48%) and also in other medical imaging tasks.

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He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Untitled work
(2014) · Cited by 33,419
Untitled work
(2008) · Journal of Machine Learning Research · Cited by 33,051
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602

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Authors: Han, C., Rundo, L., Araki, R., Nagano, Y., Furukawa, Y., Mauri, G., Nakayama, H., & Hayashi, H.
Affiliations: National Institute of Informatics (JP)
University of Cambridge (GB)
Chubu University (JP)
The University of Tokyo (JP)
Jikei University School of Medicine (JP)
University of Milano-Bicocca (IT)
International Research Center for Neurointelligence, The University of Tokyo (JP)
Kyushu University (JP)
ORCID: Han, Changhee
Rundo, Leonardo
Araki, Ryosuke
Mauri, Giancarlo
Nakayama, Hideki
Hayashi, Hideaki
Venue: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019
Volume: 7
Pages: 156966-156977
ISSN: 2169-3536 (ISSN-L)
DOI: 10.1109/access.2019.2947606
OpenAlex: W2981036610
Cited by: 253
References: 53
Citations by year: 2026: 22 · 2025: 28 · 2024: 49 · 2023: 39 · 2022: 40 · 2021: 45 · 2020: 27 · 2019: 3
FWCI: 12.69
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/legalcode (vor)
Locations: IEEE Access (publishedVersion, cc-by)
BOA (University of Milano-Bicocca) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Qdai-jump Research Program
Japan Society for the Promotion of Science (JP17K12752)
Japan Agency for Medical Research and Development (JP18lk1010028)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: generative adversarial networks, data augmentation, brain MRI, brain tumor detection, image-to-image translation, progressive growing GAN, variational autoencoder, medical image synthesis, tumor classification

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Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection

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