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Deep Learning for Survival Prediction

Technological advances create a great opportunity to provide multi-view data for patients. The goal of this project is to develop effective survival models to predict the surivals of cancer patients from the provided multi-modal data of patients.Traditional surival models mainly rely on explicitly-designed handcrafted features from medical data. This project aims to investigate if deep features extracted via deep learning can generate significant signatures for prediction of overall survival (OS) in cancer patients. Moreover, traditional survival models are unable to efficiently handle heregenerous multi-modal data of cancer patients as well as learn very complex interactions for survival predictions. In this paper, we will develop novel models to integrate multi-modal data of cancer patients for better surival prediction. The long-term goal of this project is to help improve the treatment quality of a patient based on his or her multi-modal medical data. Better survival prediction would allow clinicians to make early decisions on treatments and precision medicin.


Figure 1. An example of working flow for survival prediction from pathological images

Publication

  1. Xinliang Zhu, Jianwen Yao, Xin Luo, Guanghua Xiao, Yang Xie, Adi Gazdar and Junzhou Huang, "Lung Cancer Survival Prediction from Pathological Images and Genetic Data - An Integration Study", In Proc. of The International Symposium on Biomedical Imaging, ISBI'16, Prague, Czech Republic, April 2016.
  2. Jiawen Yao, Sheng Wang, Xinliang Zhu, Junzhou Huang, "Clinical Imaging Biomarker Discovery for Survival Prediction on Lung Cancer Imaging Genetic Data", In Proc. of the 19th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'16, Athens, Greece, October 2016. (Oral Presentation)
  3. Xinliang Zhu, Jiawen Yao and Junzhou Huang, "Deep Convolutional Neural Network for Survival Analysis with Pathological Images", In Proc. of IEEE International Conference on Bioinformatics and Biomedicine, BIBM'16, Shenzhen, China, December 2016.
  4. Xinliang Zhu, Jiawen Yao, Guanghua Xiao, Yang Xie, Jaime Rodriguez-Canales, Edwin R. Parra, Carmen Behrens, Ignacio I. Wistuba and Junzhou Huang, "Imaging-Genetic Data Mapping for Clinical Outcome Prediction via Supervised Conditional Gaussian Graphical Model", Proc. of IEEE International Conference on Bioinformatics and Biomedicine, BIBM'16, Shenzhen, China, December 2016.
  5. Jiawen Yao, Xinliang Zhu, Feiyun Zhu and Junzhou Huang, “Deep Correlational Learning for Survival Prediction from Multi-modality Data”, In Proc. of the 19th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI’17, Quebec City, Quebec, Canada, September 2017. (Oral Presentation) [CODE]
  6. Xinliang Zhu, Jiawen Yao, Feiyun Zhu and Junzhou Huang, “WSISA: Making Survival Prediction from Whole Slide Pathology Images”, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR’17, Honolulu, Hawaii, USA, July 2017. [CODE]
  7. Ruoyu Li, Sheng Wang, Feiyun Zhu and Junzhou Huang, "Adaptive Graph Convolutional Neural Networks", In Proc. of The Thirty-Second AAAI Conference on Artificial Intelligence, AAAI'18, New Orleans, USA, February 2018. (Oral Presentation) [CODE]
  8. Ruoyu Li, Jiawen Yao, Xinliang Zhu, Yeqing Li and Junzhou Huang, "Graph CNN for Survival Analysis on Whole Slide Pathological Images", In Proc. of the 20th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'18, Granada, Spain, September 2018. [CODE]
  9. Jiawen Yao, Xinliang Zhu and Junzhou Huang, "Deep Multi-Instance Learning for Survival prediction from Whole Slide Images", In Proc. of the 21st Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'19, Shenzhen, China, October 2019. [CODE]
  10. Ashwin Raju, Jiawen Yao, Mohammad Minhazul Haq, Jitendra Jonnagaddala and Junzhou Huang, "Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging", In Proc. of the 22nd Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'20, Lima, Peru, October 2020.
  11. Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala, Nicholas Hawkins and Junzhou Huang, "Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Networks", Medical Image Analysis, Volume 65, October 2020. [CODE]