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Chunbao Yuan¹, Xibin Jia¹, Luo Wang¹

¹School of Computer Science, Beijing University of Technology, Beijing, China.

Current medical imaging

|August 4, 2025

概括

SequencesNet是一种新的深度学习模型,通过捕捉微妙的细节,准确地分类腹部磁共振成像 (MRI) 序列. 这种方法通过有效处理MRI序列类型内和之间的变异来提高诊断准确性.

科学领域:

医疗成像医学成像
人工智能的人工智能
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

磁共振成像 (MRI) 对于临床诊断至关重要,提供各种组织和结构信息.
传统的深度学习方法在MRI序列识别方面遇到了困难,原因是高的类内变化和微妙的类间差异.
现有的模型往往忽略了精细细节,这些细节对于准确的MRI序列分类至关重要.

研究的目的:

提出SequencesNet,一个精细的原型网络,用于改进MRI序列分类.
解决腹部MRI序列中微妙的类间差异和显著的类内变异的挑战.
为了提高基于深度学习的MRI序列识别的准确性和可解释性.

主要方法:

开发了SequencesNet,将卷积神经网络 (CNN) 与改进的视觉转换器集成,用于特征提取.
在视觉变压器中集成了一个特征选择模块 (FSM),以使用融合的注意力权重选择细粒度的特征.
使用原型分类模块 (PCM) 来根据提取的细粒度表示来分类MRI序列.

主要成果:

序列网实现了最先进的准确性,在公共数据集上达到96.73%,在私人数据集上达到95.98%.
该模型在分类任务中表现优于比较原型和细粒度模型.
可视化证实了SequencesNet在捕获微细信息方面的卓越能力,这对于区分MRI序列至关重要.

结论:

SequencesNet在MRI序列分类方面表现出高性能,有效地管理了类内变化和类间微妙性.
特性选择模块 (FSM) 通过专注于关键细粒度特征来提高临床解释性.
序列网的模块化设计提供了扩展到其他医学成像任务的潜力,未来的工作重点是计算效率和概括.

关键词:

核磁共振成像 (MRI) 序列分类分类深度学习. 深度学习.细粒度的学习学习.原型学习学习的原型学习.

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