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预测胎儿大脑妊娠年龄使用多头注意力与Xception

Mohammad Asif Hasan¹, Fariha Haque¹, Tonmoy Roy²

¹Department of Electronics & Telecommunication Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh.

Computers in biology and medicine

|September 15, 2024

概括

这项研究引入了一个新的深度学习模型,使用胎儿MRI脑图像进行准确的妊娠年龄 (GA) 预测. 这种先进的方法结合了Xception和多头注意力,显著提高了产前护理的精度.

科学领域:

医疗成像医学成像
人工智能的人工智能
胎儿医学胎儿医学

背景情况:

准确的妊娠年龄 (GA) 预测对于监测胎儿发育和产前护理至关重要.
传统的GA预测方法往往缺乏精度和效率.
深度学习 (DL) 为提高GA预测准确性提供了一个有希望的途径.

研究的目的:

开发和评估使用胎儿大脑MRI进行GA预测的新型DL方法.
将Xception预训练模型与多头注意力 (MHA) 机制相结合,用于特征提取和预测.
评估模型在不同解剖学视图中的性能,并将其与现有的最先进 (SOTA) 方法进行比较.

主要方法:

利用了来自741名患者 (GA 19-39周) 的52,900张胎儿大脑MRI图像的数据集.
采用Xception模型进行特征提取,然后使用可配置的多头注意力 (MHA) 机制.
训练模型以在几天内预测GA,优化注意力头和键/值空间维度等参数.

主要成果:

在测试组件上实现了高精度:R平方 (R2) 为96.5%,平均绝对误差 (MAE) 为3.80天,皮尔森相关系数 (PCC) 为98.50%.
五次交叉验证证实了可靠性,平均R2为95.94%,MAE为3.61天,PCC为98.02%.
在轴向和斜视图中表现出卓越的性能,优于其他SOTA模型.

结论:

拟议的DL模型整合Xception和MHA提供了一个高度准确和可靠的方法,用于从胎儿大脑MRI中预测GA.
该模型在多种解剖学视图中的有效性表明其对于临床应用的稳定性.
这种方法具有很大的潜力,可以帮助临床医生精确地确定GA,优化产前护理.

关键词:

胎儿大脑核磁共振 (MRI) 检查结果怀孕年龄预测预测多层飞机是多层飞机.多头的注意力注意力.这是一架单飞机.Xception 接收接收接收接收

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