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可扩展的Swin变压器网络用于从不完整的MRI方式进行脑瘤细分

Dongsong Zhang¹, Changjian Wang², Tianhua Chen³

¹School of Big Data and Artificial Intelligence, Xinyang College, Xinyang, 464000, Henan, China; School of Computing and Engineering, University of Huddersfield, Huddersfield, HD13DH, UK.

Artificial intelligence in medicine

|March 10, 2024

概括

这项研究介绍了IMS2Trans,这是一种轻量级的深度学习模型,用于使用多模态MRI进行脑瘤细分. 它以不完整的数据和比现有方法更少的参数实现了更高的准确性.

科学领域:

医疗成像医学成像
人工智能的人工智能
神经科学是一个神经科学.

背景情况:

深度学习在多模式MRI中卓越,用于大脑瘤细分.
不完整的模式,在临床环境中很常见,降低了性能.
现有的方法具有诸如长时间的培训时间和不良的可扩展性等局限性.

研究的目的:

提出IMS2Trans,一个新的,轻量级和可扩展的Swin变压器网络,用于强大的脑瘤细分.
为应对多模态MRI数据中缺失的模式的挑战.
为了提高脑瘤细分的效率和准确性.

主要方法:

使用单个编码器从所有可用的模式中提取隐藏特征.
采用统一的特征提取流程,以实现有效的信息共享和融合.
开发了一个轻量级和可扩展的Swin变压器架构.

主要成果:

与mmFormer相比,在BraTS 2018和2020数据集上获得了更高的子相似系数 (DSC) 分数.
证明了统计学上显著的性能改善 (p值得到确认).
显著降低了模型复杂性 (4.47M参数与mmFormer的34.96M相比).

结论:

由于其单个编码器设计,IMS2Trans提供了可扩展性的优势.
精简的方法导致了轻量级和高效的网络架构.
公共可用的代码和权重有助于进一步的研究和应用.

关键词:

大脑瘤的细分脑瘤的细分不完整的模式不完整的模式变压器变压器变压器

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