mlsanityData to 14 Oct 2026

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

arXiv 1905.13456 ↗First posted 31 May 2019eess.IV cs.AI cs.CVcited by 24

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Abstract (arXiv)

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 X 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.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 May 2019.

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Citing papers per year: 2021: 11; 2022: 5; 2023: 3; 2024: 1; 2025: 2; 2026: 2.

24 papers.

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  1. 2021Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentationcited by 88
  2. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  3. 2021Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative modelscited by 36
  4. 2021GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Reviewcited by 25
  5. 2021Free-form tumor synthesis in computed tomography images via richer generative adversarial networkcited by 16
  6. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
  7. 2021Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Reviewcited by 15
  8. 2022Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neurosciencecited by 11
  9. 2021Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentationcited by 9
  10. 2023Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Reviewcited by 7
  11. 2023Generative Adversarial Networks for Data Augmentationcited by 7
  12. 2024DynaSeg: A Deep Dynamic Fusion Method for Unsupervised Image Segmentation Incorporating Feature Similarity and Spatial Continuitycited by 4
12 more
  1. 2021Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networkscited by 2
  2. 2021Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal imagecited by 2
  3. 2022Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagerycited by 2
  4. 2021Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Imagescited by 1
  5. 2022Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Imagescited by 1
  6. 2021BAPGAN: GAN-based Bone Age Progression of Femur and Phalange X-ray Imagescited by 0
  7. 2022Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Imagescited by 0
  8. 2022Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Modelscited by 0
  9. 2025HSMix: Hard and Soft Mixing Data Augmentation for Medical Image Segmentationcited by 0
  10. 2025Improving Cardiac Risk Prediction Using Data Generation Techniquescited by 0
  11. 2026Diffusion-Based Tumor Inpainting for Renal Segmentation under Clinical Data Scarcitycited by 0
  12. 2026BrainAid: Efficient Brain Tumor Detection Using Lightweight CNNs and Explainable AI for Resource-Constrained Environmentscited by 0