标题： GANet-Seg：基于混合生成模型的对抗学习在脑肿瘤分割中的应用 显示英文标题

标题： GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models

摘要： 这项工作介绍了一种新的框架，用于利用预训练的GAN和Unet架构进行脑肿瘤分割。 通过将全局异常检测模块与精炼的掩码生成网络相结合，所提出的模型能够准确识别肿瘤敏感区域，并通过对抗损失约束迭代增强分割精度。 采用多模态MRI数据和合成图像增强来提高鲁棒性，并解决标注数据有限的挑战。 在BraTS数据集上的实验结果表明了该方法的有效性，在病变级别的Dice和HD95指标上均优于基线方法。 这种可扩展的方法减少了对完全标注数据的依赖，为临床环境中实际的现实应用铺平了道路。 显示英文摘要

摘要： This work introduces a novel framework for brain tumor segmentation leveraging pre-trained GANs and Unet architectures. By combining a global anomaly detection module with a refined mask generation network, the proposed model accurately identifies tumor-sensitive regions and iteratively enhances segmentation precision using adversarial loss constraints. Multi-modal MRI data and synthetic image augmentation are employed to improve robustness and address the challenge of limited annotated datasets. Experimental results on the BraTS dataset demonstrate the effectiveness of the approach, achieving high sensitivity and accuracy in both lesion-wise Dice and HD95 metrics than the baseline. This scalable method minimizes the dependency on fully annotated data, paving the way for practical real-world applications in clinical settings.