ArticleFrontiers in neuroscience2022
A transformer-based generative adversarial network for brain tumor segmentation.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- YOLO-LS: a novel deep learning framework for brain tumor segmentation in Magnetic Resonance Imaging.Scientific reports · 2026Article
- CA-3DTransUNet with dynamic cross-scale fusion for pulmonary nodule segmentation.Scientific reports · 2026Article
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- Generative adversarial DacFormer network for MRI brain tumor segmentation.Scientific reports · 2025Article
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Transformers for Neuroimage Segmentation: Scoping Review.Journal of medical Internet research · 2025Article
- Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation.New microbes and new infections · 2024Article
- Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment.NPJ precision oncology · 2024Review
- Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.Annals of medicine and surgery (2012) · 2024Article
- Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey.Frontiers in bioengineering and biotechnology · 2024Review
- Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- SW-UNet: a U-Net fusing sliding window transformer block with CNN for segmentation of lung nodules.Frontiers in medicine · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary to CNNs. In this paper, we proposed a novel transformer-based generative adversarial network to automatically segment brain tumors with multi-modalities MRI. Our architecture consists of a generator and a discriminator, which is trained in min-max game progress. The generator is based on a typical "U-shaped" encoder-decoder architecture, whose bottom layer is composed of transformer blocks with Resnet. Besides, the generator is trained with deep supervision technology. The discriminator we designed is a CNN-based network with multi-scale
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