基于坐标的位置嵌入,捕获分辨率,以提高变压器在医学图像分析中的性能
Badhan Kumar Das1,2, Gengyan Zhao3, Saahil Islam4,5
1Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany. badhankumar.das@siemens-healthineers.com.
Scientific reports
|April 23, 2024
概括
这项研究引入了一种新的基于坐标的嵌入,用于医疗成像中的视觉转换器 (ViTs). 这种方法通过保持空间分辨率和利用几何信息来提高心脏病发作细分的准确性.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 视觉转换器 (ViTs) 通过捕捉全球依赖,在计算机视觉方面表现出色.
- 医学成像数据通常需要重新采样,冒着空间分辨率损失的风险.
- 现有的ViT架构面临着医疗图像尺寸和分辨率变化的挑战.
研究的目的:
- 建议在医学成像中基于坐标嵌入视觉转换器.
- 为了克服医疗图像预处理中重新采样和调整尺寸的局限性.
- 提高基于变压器的模型在医学图像分析中的性能.
主要方法:
- 开发了一个基于坐标的嵌入式编码图像几何,物理坐标和分辨率.
- 在UNETR和SwinUNETR模型中整合了拟议的嵌入.
- 评估了MRI数据集上的模型,用于使用AxTrace和AxADC对比的心脏病细分.
主要成果:
- 基于坐标的嵌入显著改善了平均子得分的6.5% (UNETR) 和7.6% (SwinUNETR).
- 该方法比其他方法具有统计学上显著的优势 (p值<0.0001).
- 在不影响空间分辨率或不需要重新采样的情况下实现了增强的性能.
结论:
- 拟议的基于坐标的像素智能定位嵌入是基于变压器的医疗图像分析的一个有希望的解决方案.
- 这种技术有效地利用物理坐标信息来提高性能.
- 它为未来在医疗应用中位置嵌入方面的进步奠定了基础.
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