用BI-RADS术语描述乳房超声波图像的深度学习.
Mikel Carrilero-Mardones1, Manuela Parras-Jurado2, Alberto Nogales3
1Department of Artificial Intelligence, Universidad Nacional de Educacion a Distancia (UNED), Madrid, Spain. mcarrilero@dia.uned.es.
Journal of imaging informatics in medicine
|June 26, 2024
概括
这项研究引入了深度神经网络,以帮助使用乳腺成像报告和数据系统 (BI-RADS) 诊断乳腺癌. 人工智能系统检测,描述和分类瘤,提高诊断准确性并提供可解释的结果.
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:
- 乳腺癌是女性最常见的癌症,需要准确的诊断工具.
- 超声波解释需要专家知识,突出需要计算机辅助诊断 (CAD) 系统.
- 乳腺成像报告和数据系统 (BI-RADS) 为描述瘤和评估恶性瘤提供了一个标准化的语言.
研究的目的:
- 开发和评估深度神经网络,用于自动检测,描述和分类乳腺超声波图像中的瘤.
- 通过整合BI-RADS描述符来提高AI驱动恶性瘤分类的可解释性.
- 提高乳腺癌评估的诊断准确性和一致性.
主要方法:
- 一个深度神经网络在749个专家注释的公共数据集节点上受过训练.
- 使用YOLO检测算法进行兴趣区域 (ROI) 提取.
- 一个多类分类模型处理ROI以输出BI-RADS描述符,BI-RADS分类和恶性瘤预测.
主要成果:
- 与其他最先进的CNN相比,拟议的模型与专家放射科医生 (科恩卡帕:0.58在交叉验证中,0.64在测试中) 达成了更好的协议.
- 整合YOLO算法显著改善了模型性能.
- 使用BI-RADS描述符的训练使得可以解释恶性瘤的分类,而不会影响准确性.
结论:
- 开发的深度神经网络通过基于BI-RADS标准的自动瘤分析,有效地帮助诊断乳腺癌.
- 该系统提供可解释的AI,增强医生对诊断过程的信任和理解.
- 这种方法代表了乳房超声波计算机辅助诊断的重大进步,提高了准确性和可解释性.
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