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Updated: Jan 16, 2026

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一种机器学习方法用于对死后图像进行自动伤害分类.

Kamila Barbara Kalinowska¹, Dorota Zawieska¹, Sebastian Puchała²

¹Department of Photogrammetry, Remote Sensing and Spatial Information Systems, Warsaw University of Technology, Politechniki 1 Square, 00-661, Warsaw, Poland.

Journal of forensic and legal medicine

|September 28, 2025

概括

人工智能 (AI) 通过自动化死后伤害检测,特别是伤和擦伤来帮助法医科学. 人工智能模型实现了高精度,改善了对法医图像的客观分析.

关键词:

分类分类分类分类.深度学习是一种深度学习.法医医学法医学的医学.伤害伤害伤害伤害

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科学领域:

法医科学法医科学法医科学
医疗成像医学成像
计算机视觉计算机视觉

背景情况:

法医科学需要对死后伤害进行客观分析.
自动检测伤害,如伤和擦伤,可以提高准确性和效率.
人工智能 (AI) 为法医图像分析提供了新的机会.

研究的目的:

开发和评估人工智能模型用于死后伤和擦伤的语义细分.
调查用于法医伤害检测的不同深度学习架构的性能.
优化人工智能模型以提高死后图像分析的准确性.

主要方法:

收集并预处理了一组死后伤害图像的数据集.
实现并比较了三个深度学习架构:U-Net,FPN和LinkNet,与EfficientNetB3和ResNet50的骨干.
利用自定义的损失函数,图像转换和类平衡来进行模型优化.

主要成果:

性能最好的AI模型在检测死后伤害方面实现了高灵敏度 (92.7%) 和特异性 (98.9%).
证明了人工智能驱动的语义细分在伤和擦伤之间区分的有效性.
人工智能模型显示了自动化和客观的死后图像分析的巨大潜力.

结论:

人工智能驱动的方法为在伤害检测中对死后图像进行自动和客观分析提供了有希望的方法.
开发的AI模型可以帮助法医专家识别和分类死后伤害.
进一步的研究可以建立在这些发现的基础上,以推进AI在法医病理学中的应用.