Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.

ABSTRACT: 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 predic

SUBMITTER: Yen TJ 

PROVIDER: S-EPMC10973492 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature