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可解释的自动化TI-RADS评估甲状腺结节

Alisa Kunapinun^1,2, Dittapong Songsaeng², Sittaya Buathong²

¹Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, FL 34946, USA.

Sensors (Basel, Switzerland)

|August 26, 2023

概括

机器学习模型使用甲状腺成像报告和数据系统 (TI-RADS) 和Grad-CAM可视化,提高甲状腺结节分类准确度高达10%. 这提高了恶性瘤的检测,并有助于临床评估.

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:

甲状腺结节很常见,需要准确的风险分层来进行适当的管理.
甲状腺成像报告和数据系统 (TI-RADS) 提供了一个标准化的框架,用于根据超声波特征对甲状腺结节进行分类.
在TI-RADS中存在局限性,特别是训练数据稀缺,需要补充诊断工具.

研究的目的:

开发和验证一种自动化系统,使用TI-RADS标准对甲状腺结节进行分类,并评估恶性瘤风险.
研究机器学习模型 (ResNet-101,DenseNet-201) 与Grad-CAM一起用于改进TI-RADS分类的有效性.
通过将人工智能驱动的见解与临床指南相结合,提高甲状腺结节评估的诊断准确性.

主要方法:

实施ResNet-101和DenseNet-201深度学习模型用于甲状腺结节的分类.
利用Grad-CAM算法来分析模型决策,并确定与TI-RADS评分相关的关键节点特征.
通过将Grad-CAM结果与功能概率计算进行集成来开发精确的热图,以增强可视化.

主要成果:

自动化系统实现了提升TI-RADS分类准确度,在使用ResNet-101和DenseNet-201模型时提升了高达10%.
在Grad-CAM分析中,成功地确定了有助于TI-RADS得分的风险区域和结节特征,证明了模型的可解释性.
综合方法提供了一个视觉热图,有助于识别与恶性瘤评估相关的特定结节特征.

结论:

机器学习模型,特别是ResNet-101和DenseNet-201与Grad-CAM相结合,在TI-RADS框架内显著提高了甲状腺结节分类的准确性.
开发的系统为协助临床医生准确诊断和甲状腺结节的风险分层提供了一个有前途的工具.
这种人工智能驱动的方法的进一步探索和临床整合有可能促进甲状腺癌检测和患者护理的进步.

关键词:

在DenseNet中,使用的是DenseNet.这是Grad-CAM.这就是ResNet ResNet.这就是TI-RADS.这是分类分类的分类.深度学习是一种深度学习.热图是一种热图.甲状腺结节甲状腺结节

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