  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).
