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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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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