ArticleScientific reports2022
Brain tumor image generation using an aggregation of GAN models with style transfer.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A Systematic Review on Synthetic Medical Images Generation-Recent Trends and Future Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- A Comprehensive Review of Artificial Intelligence for Brain Tumor Analysis: Taxonomy, Robustness, and Open Challenges in Neuro-Oncology.Journal of imaging · 2026Review
- An Efficient and Cohesive System for Enhanced Accuracy in Malignant Brain Tumor Diagnosis.Current medical imaging · 2026Article
- Localization-guided segmentation of Gliomas via 3D ReFusionNet.Frontiers in oncology · 2026Article
- Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.International journal of biomedical imaging · 2026Review
- A Study on the Performance Comparison of Brain MRI Image-Based Abnormality Classification Models.Life (Basel, Switzerland) · 2025Article
- High-resolution conditional MR image synthesis through the PACGAN framework.Scientific reports · 2025Article
- Generative adversarial DacFormer network for MRI brain tumor segmentation.Scientific reports · 2025Article
- Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies.IEEE open journal of engineering in medicine and biology · 2025Article
- Using a patient-specific diffusion model to generate CBCT-based synthetic CTs for CBCT-guided adaptive radiotherapy.Medical physics · 2025Article
- Adapted generative latent diffusion models for accurate pathological analysis in chest X-ray images.Medical & biological engineering & computing · 2024Article
- Article
- BrainCDNet: a concatenated deep neural network for the detection of brain tumors from MRI images.Frontiers in human neuroscience · 2024Article
- Enhanced MRI-based brain tumour classification with a novel Pix2pix generative adversarial network augmentation framework.Brain communications · 2024Article
- Multi-Layer Preprocessing and U-Net with Residual Attention Block for Retinal Blood Vessel Segmentation.Diagnostics (Basel, Switzerland) · 2023Article
- Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment.Japanese journal of radiology · 2023Article
- Multimodal data fusion for cancer biomarker discovery with deep learning.Nature machine intelligence · 2023Article
- A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation.Medicina (Kaunas, Lithuania) · 2023Article
- Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis.Diagnostics (Basel, Switzerland) · 2022Article
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5 authors.
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Abstract
In the recent past, deep learning-based models have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated datasets. An interesting application of deep learning is synthetic data generation, especially in the domain of medical image analysis. The need for such a task arises due to the scarcity of original data. Class imbalance is another reason for applying data augmentation techniques. Generative Adversarial Networks (GANs) are beneficial for synthetic image generation in various fields. However, stand-alone GANs may only fetch the localized features in the latent representation of an image, whereas combining different GANs might understand the distributed features. To this end, we have proposed AGGrGAN, an aggregation of three base GAN models-two variants of Deep Convolutional Generative Adversarial Network (DCGAN) and a Wasserstein GAN (WGAN) to generate synthetic MRI scans of brain tumors. Further, we have applied the style transfer technique to enhance the image resemblance. Our proposed model efficiently overcomes the limitation of data unavailability and can understand the information variance in multiple representations of the raw images. We have conducted all the experiments on the two publicly available datasets - the brain tumor dataset and the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. Results show that the proposed model can generate fine-quality images with maximum Structural Similarity Index Measure (SSIM) scores of 0.57 and 0.83 on the said two datasets.
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