Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks

TL;DR

This paper introduces a deep learning framework that leverages multiple ultrasound images and indicator-guided learning to accurately predict liver fibrosis stages, improving diagnostic performance for chronic hepatitis B patients.

Contribution

It presents a novel multi-indicator guided deep neural network that enhances interpretability and accuracy in liver fibrosis diagnosis from ultrasound data.

Findings

Achieved 65.6% accuracy, outperforming previous methods by 20%.

Utilized a comprehensive dataset of 229 patients with ultrasound videos/images, indicators, and labels.

Demonstrated state-of-the-art performance in liver fibrosis prediction.

Abstract

Accurate analysis of the fibrosis stage plays very important roles in follow-up of patients with chronic hepatitis B infection. In this paper, a deep learning framework is presented for automatically liver fibrosis prediction. On contrary of previous works, our approach can take use of the information provided by multiple ultrasound images. An indicator-guided learning mechanism is further proposed to ease the training of the proposed model. This follows the workflow of clinical diagnosis and make the prediction procedure interpretable. To support the training, a dataset is well-collected which contains the ultrasound videos/images, indicators and labels of 229 patients. As demonstrated in the experimental results, our proposed model shows its effectiveness by achieving the state-of-the-art performance, specifically, the accuracy is 65.6%(20% higher than previous best).