多模态图形神经网络用于镜数据分类和可视化
Priyadarshini Chatterjee1, Shadab Siddiqui1, Razia Sulthana Abdul Kareem2
1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad 500075, Telangana, India.
Cancers
|May 14, 2025
概括
这项研究引入了用于宫病变分类的新型图形神经网络 (GNN) 框架,通过整合多模式数据显著提高了准确性. 先进的GNN模型增强了早期宫癌检测能力.
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:
- 宫病变的分类对于早期发现宫癌至关重要.
- 当前的深度学习模型通常使用单模数据或需要广泛的手动注释.
- 一个新的图形神经网络 (GNN) 框架被提议用于整合多模式数据以进行增强的分类.
研究的目的:
- 开发和评估一个基于GNN的框架,用于宫病变的分类.
- 通过使用多模式数据,提高宫癌检测的准确性和效率.
- 探索基于图形的多模式学习在临床瘤学的潜力.
主要方法:
- 开发了一个完全连接的基于图形的架构,使用GCNConv层和全球平均值聚合.
- 模型优化使用网格搜索进行,性能通过五倍交叉验证进行评估.
- 该框架集成了colposcopy图像,细分面具和图形表示.
主要成果:
- 在微调之前,GNN模型实现了89.4%的宏观平均F1得分和92.1%的验证准确性.
- 在微调后,性能提高到94.56% (F1得分) 和98.98% (精度).
- 基于LIME的可视化证实了该模型的重点是歧视性损伤区域.
结论:
- 基于图形的多模式学习显示了宫病变分析的巨大潜力.
- 开发的框架显示出在宫癌查中临床应用的前景.
- 与MNJ瘤研究所等临床机构的合作对翻译研究有价值.
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