基于视觉变压器的多式融合网络,用于乳腺超声波上的瘤恶性瘤的分类:一项回顾性多中心研究
Mengying Li1, Yin Fang1, Jiong Shao1
1School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, PR China.
International journal of medical informatics
|January 25, 2025
概括
一个新的多式模式模型使用超声波图像准确预测乳腺瘤恶性病变,结合临床数据和深度学习功能. 这种方法显著提高了诊断准确度,有助于诊断乳腺癌的临床决策.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 精确区分良性和恶性乳腺质量对于常规乳腺癌诊断至关重要.
- 很少有研究将成像组织学,深度学习和临床参数用于此目的.
- 超声波成像是评估乳腺质量的关键方式.
研究的目的:
- 开发和验证使用超声波图像预测乳腺瘤恶性病变的多式特征融合模型.
- 为了利用深度学习特征和临床参数以及成像特征.
- 为了提高诊断准确度,区分良性和恶性乳腺质量.
主要方法:
- 这是一项回顾性研究,使用了1065名患者和3315名超声波图像数据集的数据集.
- 开发一个综合临床特征,深度学习特征和成像组织学的多式模式.
- 实验工作流涉及单模模型优化,特征提取,多模融合和分类.
主要成果:
- 多式联络模型在初级多中心数据集上取得了0.994的AUC和0.971的F1得分.
- 在独立测试队列 (TC1 AUC: 0.942,F1: 0.872;TC2 AUC: 0.945,F1: 0.857) 上保持强的表现.
- 与替代方法相比,决策曲线分析显示在特定概率范围内具有更高的准确性.
结论:
- 拟议的多式模式有效地整合了各种患者数据,用于乳腺瘤恶性瘤预测.
- 该模型在分类良性和恶性乳腺超声波瘤方面表现出高性能.
- 这种方法为诊断乳腺癌的临床决策提供了显著的好处.
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