提升前列腺癌诊断:在代表性不足的人口中进行多类分类的ConvNeXt方法
Declan Ikechukwu Emegano1, Mubarak Taiwo Mustapha1, Ilker Ozsahin1
1Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey.
Bioengineering (Basel, Switzerland)
|April 26, 2025
概括
这项研究引入了ConvNeXt用于分类前列腺癌图像,达到98%的准确性. 这种先进的AI模型改善了代表性不足的地区的诊断,促进了全球公平的医疗保健.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 前列腺癌对全球健康造成重大负担,在撒哈拉以南非洲等代表性不足的地区存在诊断差异.
- 不同数据集的有限可用性阻碍了人工智能驱动的癌症诊断的公平进展.
研究的目的:
- 开发和验证一种新的ConvNeXt深度学习模型,用于前列腺组织病理图像的多类分类.
- 通过利用尼日利亚和 ProstateX 数据集的数据来解决诊断数据集中缺乏区域代表性的问题.
主要方法:
- 使用ConvNeXt架构将前列腺组织病理图像分为正常,良性和恶性类别.
- 该模型结合了先进的数据增强,Grad-CAM用于可解释性,以及对优化进行废除研究.
- 对传统的CNN和变压器模型进行性能评估,并在 ProstateX 数据集上进行验证.
主要成果:
- 在尼日利亚数据集上,ConvNeXt模型实现了98%的准确性,超过了ResNet50,EfficientNet,DenseNet,ViT,CaiT,Swin Transformer和RegNet.
- 对前列腺X数据集的验证结果为87.2%的准确性,85.7%的回忆,86.4%的F1得分和0.92的AUC.
- 格拉德-CAM可视化提供了临床可解释性,并且除研究证实了该模型的稳定性.
结论:
- ConvNeXt在前列腺癌组织病理学分类中表现出卓越的性能,提供了强大的和可解释的解决方案.
- 该研究强调了人工智能的潜力,以改善低资源环境和代表性不足的人群中的癌症诊断.
- 这项工作通过促进全球癌症诊断研究的包容性来推进公平的医疗保健.
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