对于多种组织学类型的卵巢瘤,深度卷积神经网络在超声波图像中的分类
Meijing Wu1, Guangxia Cui1, Shuchang Lv2
1The Department of Gynecology and Obstetrics, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Frontiers in oncology
|July 10, 2023
概括
深层卷积神经网络 (DCNNs) 在从超声波图像中分类卵巢瘤方面表现有前途. 该ResNext50模型实现了高精度,有助于诊断.
科学领域:
- 医学成像分析分析 医学成像分析
- 人工智能在瘤学中的应用
- 妇科病理学的病理学.
背景情况:
- 精确分类卵巢瘤对于有效治疗至关重要.
- 超声波 (US) 影像是卵巢瘤评估的主要方式.
- 区分良性和恶性卵巢瘤,特别是高度血清癌,至关重要.
研究的目的:
- 在超声波 (US) 图像中评估和验证深卷积神经网络 (DCNNs) 用于分类卵巢瘤的组织学类型.
- 使用转移学习来比较各种预训练的DCNN模型的性能.
- 评估DCNN在为卵巢瘤提供计算机辅助诊断信息方面的实用性.
主要方法:
- 一项回顾性研究使用了来自328名患者的1142张美国图像.
- 定义了两个任务:对良性与高度血清癌的分类,以及使用细分图像对七种组织学类型的详细分类.
- 六个经过预先培训的DCNN (VGG16,GoogleNet,ResNet34,ResNext50,DensNet121,DensNet201) 被使用转移学习.
主要成果:
- 与原始图像相比,DCNN在分割的美国图像上表现出优异的性能.
- ResNext50模型实现了最高的预测性能,在分类七种组织学类型时,整体准确率为0.952.
- 对于高度血清癌,ResNext50模型报告了90%的敏感性和99.2%的特异性;良性类别显示>90%的敏感性和>95%的特异性.
结论:
- 在美国图像中,DCNN代表了一种有希望的人工智能技术,用于对卵巢瘤的分类.
- 该研究强调了DCNN提供有价值的计算机辅助诊断信息的潜力,提高了诊断准确度.
- 进一步验证和整合到临床工作流程中可以提高卵巢瘤的诊断和管理.
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