Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
Sci Rep. 2024 Mar 28;14(1):7345. doi: 10.1038/s41598-024-57386-3.
Ultrasound imaging is a widely used technique for fatty liver diagnosis as it is practically affordable and can be quickly deployed by using suitable devices. When it is applied to a patient, multiple images of the targeted tissues are produced. We propose a machine learning model for fatty liver diagnosis from multiple ultrasound images. The machine learning model extracts features of the ultrasound images by using a pre-trained image encoder. It further produces a summary embedding on these features by using a graph neural network. The summary embedding is used as input for a classifier on fatty liver diagnosis. We train the machine learning model on a ultrasound image dataset collected by Taiwan Biobank. We also carry out risk control on the machine learning model using conformal prediction. Under the risk control procedure, the classifier can improve the results with high probabilistic guarantees.
超声成像是一种广泛应用于脂肪肝诊断的技术，因为它在实际中具有成本效益，并且可以使用合适的设备快速部署。当应用于患者时，会生成多个目标组织的图像。我们提出了一种基于多幅超声图像的脂肪肝诊断的机器学习模型。该机器学习模型使用预训练的图像编码器从超声图像中提取特征。它进一步通过图神经网络对这些特征生成一个摘要嵌入。该摘要嵌入被用作脂肪肝诊断分类器的输入。我们在台湾生物银行收集的超声图像数据集上训练了机器学习模型。我们还使用一致性预测对机器学习模型进行风险控制。在风险控制过程中，分类器可以在具有高概率保证的情况下提高结果。
