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深度转移学习与增强功能融合用于检测X射线图像中的异常

Zaenab Alammar^1,2, Laith Alzubaidi^2,3,4, Jinglan Zhang^1,2

¹School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia.

|August 12, 2023

概括

本研究介绍了一种用于医学图像分类的新型转移学习 (TL) 方法,其性能优于标准的ImageNet TL. 该方法有效地解决了医疗AI中有限的标记数据挑战,提高了诊断准确度.

科学领域:

医疗成像医学成像
人工智能的人工智能
机器学习机器学习

背景情况:

医疗图像分类受到稀缺的标记训练数据的阻碍,影响深度学习 (DL) 模型的性能.
转移学习 (TL) 缓解了数据稀缺性,但经常面临域不匹配问题,特别是基于ImageNet的模型.
肌肉骨放射图的准确分类,例如手腕和手腕X射线,由于数据限制,仍然具有挑战性.

研究的目的:

为医疗图像分类开发一种新的TL方法,克服ImageNet.Net的域名限制.
提高DL模型在医疗成像任务中的准确性和概括性,使用有限的标记数据.
与传统的TL技术相比,验证提议的TL方法的有效性和稳定性.

主要方法:

实施了一项新的TL策略,预训练DL模型在与目标领域相似的多种医疗图像上.
模型在小型注释医疗数据集上进行了微调,随后进行特征提取和融合,用于机器学习 (ML) 分类器.
该方法在MURA数据集上进行了评估,用于部和手腕X射线分类,使用Grad-CAM和LIME等可视化工具进行验证.

主要成果:

提议的TL方法实现了高准确度的部 (87.85%) 和手腕 (85.58%) 的分类,与相应的F1分数和科恩的卡帕系数.
用新型TL方法训练的模型显著优于使用基于ImageNet的TL的模型.
可视化技术证实了拟议方法的卓越准确性和稳定性.

结论:

开发的TL方法有效地解决了医疗图像分类中有限的标记数据的挑战.
与基于ImageNet的TL相比,拟议的方法表现出卓越的性能和稳定性,为医疗应用提供了更合适的替代方案.
TL和特征融合技术在各种医学成像任务,包括CT扫描中显示出广泛应用的希望.

关键词:

卷积神经网络 (CNN) 是一个神经网络.数据稀缺性数据稀缺性深度学习是一种深度学习.功能融合功能融合功能基于梯度的类激活热图.机器学习是机器学习.肌肉骨X射线扫描肌肉骨X射线转移学习转移学习

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