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分析和检测前列腺癌的深度学习技术:现状调查

Olushola Olawuyi¹, Serestina Viriri¹

¹School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 3629, South Africa.

Journal of imaging

|August 27, 2025

概括

这篇评论探讨了从医学图像中检测前列腺癌的机器学习和深度学习. 它强调了当前深度学习技术的挑战和评估,

科学领域:

医学成像
人工智能
癌症学

背景情况:

前列腺癌的诊断依赖于手动的图像解释,
人类视觉解释的局限性可能导致不必要的活检.
计算机技术的进步使得机器学习 (ML) 和深度学习 (DL) 成为医学图像分析的工具.

研究的目的:

用医学图像对前列腺癌的诊断技术进行全面审查.
帮助研究人员开发精确和自主的前列腺癌检测模型.
在分析复杂的前列腺损伤图像时遇到的挑战.

主要方法:

审查前列腺病变检测问题和分析方法.
对分类方法和卷积神经网络 (CNN) 架构的研究.
探索深度学习 (DL) 在基于图像的前列腺癌诊断中的应用.

主要成果:

在分析独特和复杂的前列腺损伤图像时发现了挑战.
审查了最先进的DL模型,在病变检测方面显示出有希望的结果.
研究了CNN架构及其在前列腺癌诊断中的作用.

结论:

深度学习技术可以提高前列腺癌诊断的准确性.
由于医疗图像的复杂性,目前的DL方法面临挑战.
需要进一步的研究来评估DL在前列腺癌检测中的有效性和局限性.

关键词:

深度学习前列腺癌前列腺癌检测技术现状调查

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