Determining and Validating Population Differences in Magnetic Resonance Angiography Using Sparse Representation.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2022-12-01 DOI:10.1109/bibm55620.2022.9994989

Steve Mendoza, Fabien Scalzo, Aichi Chien

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Abstract

Goal: Identifying population differences can serve as an insightful tool for diagnostic radiology. To do so, a reliable preprocessing framework and data representation are vital.

Methods: We build a machine learning model to visualize gender differences in the circle of Willis (CoW), an integral part of the brain's vasculature. We start with a dataset of 570 individuals and process them for analysis using 389 for the final analysis.

Results: We find statistical differences between male and female patients in one image plane and visualize where they are. We can see differences between the right and left-hand sides of the brain confirmed using Support Vector Machines (SVM).

Conclusion: This process can be applied to detect population variations in the vasculature automatically.

Significance: It can guide debugging and inferring complex machine learning algorithms such as SVM and deep learning models.

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利用稀疏表示确定和验证磁共振血管造影的人群差异。

目的:确定人群差异可以作为诊断放射学的一个有见地的工具。为此，可靠的预处理框架和数据表示是至关重要的。方法:我们建立了一个机器学习模型来可视化威利斯圈(CoW)的性别差异，威利斯圈是大脑脉管系统的组成部分。我们从570个人的数据集开始，使用389个人进行最终分析。结果:我们发现男性和女性患者在一个图像平面上的统计差异，并可视化他们的位置。通过支持向量机(SVM)，我们可以看到左右脑的差异。结论:该方法可用于血管种群变异的自动检测。意义:对SVM、深度学习模型等复杂机器学习算法的调试和推理具有指导意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings. IEEE International Conference on Bioinformatics and Biomedicine

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