GANet-Seg：基于混合生成模型的对抗学习脑肿瘤分割
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
AI总结：
本研究提出GANet-Seg框架，结合预训练GAN与Unet架构，利用全局异常检测与掩码生成网络，通过对抗损失约束提升脑肿瘤分割精度，在BraTS数据集上表现优于基线，减少对标注数据的依赖。
摘要：
本研究引入了一个利用预训练GAN和Unet架构的脑肿瘤分割新框架。通过将全局异常检测模块与精细的掩码生成网络相结合，所提出的模型准确识别肿瘤敏感区域，并利用对抗损失约束迭代提升分割精度。采用多模态MRI数据和合成图像增强来提升鲁棒性，解决标注数据集有限的挑战。在BraTS数据集上的实验结果证明了该方法的有效性，在病灶级Dice和HD95指标上的敏感度和准确度均高于基线。该可扩展方法最小化了对全标注数据的依赖，为临床环境中的实际应用铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
