Deep learning for clinical decision-making

Early disease diagnosis and prediction whether a tumor is likely to be responsive or not to treatment are one of the most critical yet important tasks for individualized cancer care. In this research topic, we are working on developing advanced machine learning-based strategies to effectively identify and seamlessly combine prognostic information carried by radiomics, genomics, and biomarkers in other modalities. The research work conducted in my lab focuses on addressing the challenges of small data set; data imbalance; and combination of information from multimodal data. The research achievements serve as clinical decision-making tools and form the basis of using non-invasive imaging techniques for treatment outcome prediction and discovering correlations with genetic mechanisms, leading to more accurate patient stratification.

1. Pre-treatment image-based head neck cancer treatment outcome prediction

2. Prognosis of Oropharyngeal Squamous Cell Carcinoma with MicroRNAs

3. Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers

4. Deep-learning based Multi-loss Multi-class classification

Related publications

  1. Maliazurina Binti Saad, Shenghua He, Wade Thorstad, Hiram Gay, Yujie Zhao, Su Ruan, Xiaowei Wang, Hua Li*, “Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers”, IEEE Transactions on Radiation and Plasma Medical Sciences (TRPMS), under revision, 2021.
  2. Zong Fan, Shenghua He, Su Ruan, Xiaowei Wang, Hua Li*, “Deep Learning-based Multi-Class COVID-19 Classification with X-ray Images” SPIE Medical Imaging Conference Proceedings, 2021, oral presentation.
  3. Shenghua He, Chunfeng Lian, Wade Thorstad, Hiram Gay, Yujie Zhao, Su Ruan, Xiaowei Wang, Hua Li*, “A Novel Machine Learning Approach for the Prognosis of Oropharyngeal Squamous Cell Carcinoma with MicroRNA Biomarkers”, Bioinformatics, Accepted, 2021.
  4. Amine Amyar, Romain Modzelewski, Hua Li, Su Ruan, “Multi-task Deep Learning based CT Imaging Analysis for COVID-19 Pneumonia: Classification and Segmentation”, Computers in Biology and Medicine, 2020, 104037.
  5. Xinyi Liu, Ping Liu, Rebecca D. Chernock, Zhenming Yang, Krystle A. Lang Kuhs, James S. Lewis Jr., Jingqin Luo, Hua Li, Hiram A. Gay, Wade L. Thorstad, Xiaowei Wang, “A MicroRNA Expression Signature as Prognostic Marker for Oropharyngeal Squamous Cell Carcinoma”, Journal of the National Cancer Institute, 2020.
  6. Jian Wu, Chunfeng Lian, Su Ruan, Thomas Mazur, Sasa Mutic, Mark Anastasio, Perry Grigsby, Pierre Vera, Hua Li*, “Treatment Outcome Prediction for Cancer Patients based on Radiomics and Belief Function Theory”, IEEE Trans. on Radiation and Plasma Medical Sciences (RPMS), Special issue on Machine Learning in Radiation based Medical Sciences, 2019, Vol. 3, No. 2, page: 216-244, doi: 10.1109/TRPMS.2018.2872406.
  7. Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, and Pierre Vera, “Joint Tumor Segmentation in PET-CT Images using Co-Clustering and Fusion based on Belief Functions”, IEEE Trans. on Image Processing, 2018, 28(2):755-766.
  8. Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera, “Spatial Evidential Clustering with Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images“, IEEE Trans. on Biomedical Engineering, 2018, Jan; 65(1):21-30.