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).
J. Liu and W. Wang—Equal contribution.
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Liu, J., Wang, W., Guan, T., Zhao, N., Han, X., Li, Z. (2019). Ultrasound Liver Fibrosis Diagnosis Using Multi-indicator Guided Deep Neural Networks. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_27
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DOI: https://doi.org/10.1007/978-3-030-32692-0_27
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