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MV-Swin-T: 哺乳仪分类与多视角猪变形仪

Sushmita Sarker¹, Prithul Sarker¹, George Bebis¹

¹Department of Computer Science and Engineering, University of Nevada, Reno, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|October 7, 2024

概括

这项研究引入了一种基于变压器的新型网络,用于多视图乳腺图像分类,通过保留视图间的相关性来增强乳腺癌检测. 该方法有效地整合了不同视角的信息,以提高诊断准确度.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

传统的深度学习用于乳腺癌分类通常使用单视图乳房造影,错过了放射科医生在实践中使用的关键相关性.
由于独立的视图处理或简单的融合,现有的多视图方法可能会丢失关键的视图信息.

研究的目的:

提出一个创新的多视图网络,仅基于用于乳房图像分类的变压器.
为了有效地捕捉和整合乳房镜中的面试相关性,以改善瘤检测.

主要方法:

开发了一种使用变压器的新型多视图网络.
引入了一个基于窗口的转移动态注意力块,以增强多视图信息集成.
在CBIS-DDSM和Vin-Dr Mammo数据集上评估模型.

主要成果:

基于变压器的方法证明了多视图信息的有效集成.
促进了乳房影像视图之间的空间特征的连贯转移.
对比分析显示了变压器模型在不同环境中的性能.

结论:

拟议的变压器网络解决了现有的多视图乳房学分析的局限性.
这种方法有助于更好地利用访谈相关性来改善乳腺癌分类.
这项研究为医疗图像分析中的先进人工智能提供了基础.

关键词:

乳腺质量分类的分类进行乳房显微镜.多视图多视图可以使用.变压器变压器变压器

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