论文标题
调查多模式头颈肿瘤分段的网络结构
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation
论文作者
论文摘要
受到变压器在自然语言处理和视觉变压器对计算机视觉方面的成功的启发，许多医学成像社区中的许多研究人员都涌向基于变压器的网络，用于各种主流医疗任务，例如分类，细分和估计。在这项研究中，我们分析了两个最近发表的基于变压器的网络体系结构，以完成多模式的头部和肿瘤分段的任务，并将其性能与事实上的标准3D细分网络-NNU-NET进行比较。我们的结果表明，在存在大型结构和/或需要大量视野的情况下，对长期依赖性进行建模可能会有所帮助。但是，对于诸如头颈肿瘤之类的小结构，基于卷积的U-NET结构似乎表现良好，尤其是当训练数据集很小并且计算资源受到限制时。
Inspired by the recent success of Transformers for Natural Language Processing and vision Transformer for Computer Vision, many researchers in the medical imaging community have flocked to Transformer-based networks for various main stream medical tasks such as classification, segmentation, and estimation. In this study, we analyze, two recently published Transformer-based network architectures for the task of multimodal head-and-tumor segmentation and compare their performance to the de facto standard 3D segmentation network - the nnU-Net. Our results showed that modeling long-range dependencies may be helpful in cases where large structures are present and/or large field of view is needed. However, for small structures such as head-and-neck tumor, the convolution-based U-Net architecture seemed to perform well, especially when training dataset is small and computational resource is limited.
