mlsanityData to 7 Oct 2026

A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation

arXiv 2207.14134 ↗First posted 28 Jul 2022eess.IV cs.LGcited by 4

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Abstract (arXiv)

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 with 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 are 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 L1 loss, which is proved to be effective for medical semantic image segmentation. To validate the effectiveness of our method, we conducted experiments on BRATS2015 dataset, achieving comparable or better performance than previous state-of-the-art methods.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 42 of its 72 references that are in the map. First posted on arXiv 28 Jul 2022.

Builds on

42 of 72 references.

References · 42

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30 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.

4 papers.

  1. 2023BrainVoxGen: Deep learning framework for synthesis of Ultrasound to MRIcited by 2
  2. 2023Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imagingcited by 0
  3. 2024Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformercited by 0
  4. 2026Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Papercited by 0