融合多个先进模型，提升脑肿瘤分割与图像合成精度
An Ensemble Approach for Brain Tumor Segmentation and Synthesis
- 集成nn-UNet、Swin-UNet和U-Mamba等前沿模型
- 在BraTS数据集上实现高精度分割与高质量图像合成
- 适合医学影像分析与深度学习研究者参考
机器学习在磁共振成像（MRI）尤其是神经影像中的应用正展现出巨大潜力，显著提升诊断准确性、加速图像分析并提供数据驱动的洞察，有望改善患者护理。深度学习模型通过多层处理捕捉复杂数据的细微特征，可应用于脑肿瘤分类、分割、图像合成与配准等多种任务。先前研究已证明多种模型架构（如nn-UNet、Swin-UNet）在肿瘤分割中具有高精度；基于状态空间建模的U-Mamba也在医学图像分割中表现优异。为充分利用这些先进模型，本文提出一种深度学习框架，集成多个顶尖架构，以实现精准分割并生成高质量合成图像。
原文摘要 · Abstract (English)
The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate details of complex data, which can then be used on a variety of tasks, including brain tumor classification, segmentation, image synthesis, and registration. Previous research demonstrates high accuracy in tumor segmentation using various model architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space modeling, also achieves high accuracy in medical image segmentation. To leverage these models, we propose a deep learning framework that ensembles these state-of-the-art architectures to achieve accurate segmentation and produce finely synthesized images.
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