• DOI:10.1109/ISBI48211.2021.9434170
• Corpus ID: 226226975

Multi-Modal Active Learning For Automatic Liver Fibrosis Diagnosis Based On Ultrasound Shear Wave Elastography

@article{Gao2020MultiModalAL,
  title={Multi-Modal Active Learning For Automatic Liver Fibrosis Diagnosis Based On Ultrasound Shear Wave Elastography},
  author={Lufei Gao and Rui Zhou and Changfeng Dong and Cheng Feng and Zhuguo Li and Xiang Wan and Li Liu},
  journal={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  year={2020},
  pages={410-414}
}

• Lufei Gao, Rui Zhou, +4 authors Li Liu
• Published 2 November 2020
• Computer Science
• 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD). Due to the noisy data, expensive annotations of US images, the application of Artificial Intelligence (AI) assisting approaches encounters a bottleneck. Besides, the use of single-modal US data limits the further improve of the classification results. In this work, we innovatively propose a multi-modal fusion network with active learning (MMFN-AL) for ALFD to exploit the information of multiple modalities, eliminate the noisy data and reduce the annotation cost. Four image modalities including US and three types of shear wave elastography (SWEs) are exploited. A new dataset containing these modalities from 214 candidates is collected and pre-processed, with the labels obtained from the liver biopsy results. Experimental results show that our proposed method outperforms the state-of-the-art performance using less than 30% data, and by using only around 80% data, the proposed fusion network achieves high AUC 89.27% and accuracy 70.59%.