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图像分辨率对胸部X射线自动分类的影响

Md Inzamam Ul Haque¹, Abhishek K Dubey², Ioana Danciu²

¹University of Tennessee, The Bredesen Center, Knoxville, Tennessee, United States.

Journal of medical imaging (Bellingham, Wash.)

|August 7, 2023

概括

对于胸部X射线分析的深度学习模型在使用多个图像分辨率时表现最好,而不仅仅是高分辨率图像. 结合不同的尺度可以提高分类准确性,并提取各种信息.

科学领域:

人工智能的人工智能
医学成像分析医学成像分析
计算机视觉计算机视觉

背景情况:

深度学习 (DL) 模型在自动胸部X射线 (CXR) 分析方面表现出色.
目前的模型经常使用缩小尺寸的图像由于硬件限制,尽管有高分辨率数据集.
先进的硬件可以在没有细节损失的情况下在高分辨率图像上训练DL模型.

研究的目的:

调查图像分辨率对CXR分类性能的影响.
为了评估不同的下调分辨率如何影响DL模型的准确性.
确定用于高分辨率CXR分析的最佳分辨率策略.

主要方法:

使用了MIMIC-CXR-JPG数据集 (377,110个高分辨率CXR).
应用图像缩放到原生分辨率, , , , 和 .
雇佣了DenseNet121和EfficientNet-B4DL模型,用于跨分辨率的临床任务评估.

主要成果:

一些发现在高分辨率下更好地识别,而另一些发现在缩小输入时更清晰.
需要大接收场的任务可以从缩小,低分辨率的图像中获益.
组合模型结合了多个分辨率,优于单个分辨率模型,具有可解释的尺度权重.

结论:

强调多个尺度的特征对于从高分辨率的CXR中提取信息至关重要.
仅仅依靠最好的分辨率对于CXR分类来说是不理想的.
通过不同的图像分辨率捕获各种信息,提高模型性能.

关键词:

胸部X射线胸部X射线胸部X射线深度学习是一种深度学习.图像分辨率图像分辨率图像分辨率图像分辨率多任务分类是多任务分类.接收场是一个接收场.

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