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监督的对比损失有助于发现更强大的特征,用于光声学前列腺癌识别

Yingna Chen^1,2, Feifan Li¹, Zhuoheng Dai¹

¹School of Information Engineering, College of Science & Technology Ningbo University, Ningbo, Zhejiang, China.

Frontiers in oncology

|July 24, 2025

概括

监督对比学习改善了前列腺癌 (PCa) 诊断的光声谱分析. 该SCL调整模型提高了对噪声和数据变化的准确性和弹性,优于传统方法.

科学领域:

生物医学光学生物医学光学
医学成像医学成像
机器学习在医疗保健中的应用

背景情况:

光声谱分析显示前列腺癌 (PCa) 诊断具有前景.
深度学习提高了PCa歧视的准确性,但个体异质性仍然是一个挑战.

研究的目的:

使用监督对比学习从生物组织中提取更可靠的特征.
为了提高前列腺癌检测的区分精度.

主要方法:

引入了用于光声谱特征提取的监督对比学习.
基于CNN,监督对比 (SC) 和SCL调整模型与传统方法进行了比较.

主要成果:

与传统方法相比,SCL-adjust模型实现了最佳性能,精度提高了10%以上.
提取的特征表现出对均/高斯噪声和模型转移的弹性,与CNN模型相比改善了5%左右.

结论:

监督对比学习有效地增强了用于PCa诊断的光声谱分析.
在PCa检测中,SCL-adjust模型显示了性能,抗噪声和适应PCa检测数据异质性的改进.

关键词:

在美国,CNN是CNN.摄影声学光谱分析前列腺癌是前列腺癌.强大的功能功能强大的功能.监督的对比学习学习.

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