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Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
Open accessAbstract
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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“Prior research found that generative AI created nonfactual or unfaithful data and outputs [72,77].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [72]
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“Noise-to-image and image-to-image GANs have been used to synthesize realistic training magnetic resonance imaging images to boost the performance of convolutional neural networks for image diagnostic AI [39,72].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [72]
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Metadata
- 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)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 156966-156977
- ISSN
- 2169-3536 (ISSN-L)
- 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
- 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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