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Big Image-Omics Data Analytics for Clinical Outcome Prediction

Recent technological innovations are enabling scientists to capture complex image-omics data from different views. However, the major computational challenges are due to the unprecedented scale and complexity of heterogeneous image-omics data analytics. To solve the key and challenging problems in mining such comprehensive heterogeneous image-omics data, this project proposes to develop novel large scale learning tools and explore ways to integrate features from multiple data sources for clinical outcome prediction. It will greatly support the Precision Medicine Initiative, which has become a national goal and was unveiled by the U.S. government as a research effort designed to enable physicians to select individualized treatments.

Recent studies demonstrated the feasibility and advantage of using digital pathological image analysis for objective and unbiased clinical prognosis. However, there is a lack of comprehensive pathological image analysis for cancer data due to the complexity and heterogeneity of the disease. With the advance of technology, tumor tissue histology slide scanning is becoming a routine clinical procedure, which produces massive digital pathological images that capture histological details in high resolution. In this study, we will develop novel and powerful computational approaches to analyze pathological images. We will also develop algorithms to integrate features from pathological images with clinical and molecular profiling data to predict the clinical outcomes of cancer patients.


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Publication

  1. Zheng Xu, Junzhou Huang, "Efficient Lung Cancer Cell Detection with Deep Convolution Neural Network", 1st International Workshop on Patch-based Techniques in Medical Imaging, PMI'15, Munich, Germany, October 2015.
  2. Hao Pan, Zheng Xu, Junzhou Huang, "An Effective Approach for Robust Lung Cancer Cell Detection", 1st International Workshop on Patch-based Techniques in Medical Imaging, PMI'15, Munich, Germany, October 2015.
  3. Ruoyu Li, Junzhou Huang, "Fast Regions-of-Interest Detection in Whole Slide Histopathology Images", 1st International Workshop on Patch-based Techniques in Medical Imaging, PMI'15, Munich, Germany, October 2015.
  4. Jiawen Yao, Dheeraj Ganti, Xin Luo, Guanghua Xiao, Yang Xie, Shirley Yan and Junzhou Huang, "Computer-assisted Diagnosis of Lung Cancer Using Quantitative Topology Features", 6th International Workshop on Machine Learning in Medical Imaging, MLMI'15, Munich, Germany, October 2015.
  5. Menglin Jiang, Shaoting Zhang, Junzhou Huang, Lin Yang, Dimitris Metaxas, "Joint Kernel-Based Supervised Hashing for Scalable Histopathological Image Analysis", In Proc. of the 18th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'15, Munich, Germany, October 2015. (MICCAI Young Scientist Award, 4 out of 810 submissions)
  6. 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.
  7. Zheng Xu, Junzhou Huang, "Detecting 10,000 Cells in One Second", In Proc. of the 19th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'16, Athens, Greece, October 2016.
  8. Sheng Wang, Jiawen Yao, Zheng Xu, Junzhou Huang, "Subtype Cell Detection with an Accelerated Deep Convolution Neural Network", In Proc. of the 19th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'16, Athens, Greece, October 2016.
  9. 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)
  10. Menglin Jiang, Shaoting Zhang, Junzhou Huang, Lin Yang, Dimitris Metaxas, "Scalable Histopathological Image Analysis via Supervised Hashing with Multiple Features", Medical Image Analysis, Volume 34, pp. 3-12, December 2016.
  11. 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.
  12. 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", In Proc. of IEEE International Conference on Bioinformatics and Biomedicine, BIBM'16, Shenzhen, China, December 2016.
  13. 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)
  14. Xin Luo, Faliu Yi, Junzhou Huang, Lin Yang, Yang Xie and Guanghua Xiao, “Automatic Extraction of Cell Nuclei from H&E-stained Histopathological Images”, Journal of Medical Imaging, Volume 4, pp. 4-12, June 2017.
  15. Xin Luo, Xiao Zang, Lin Yang, Junzhou Huang, Faming Liang, Jaime Rodriguez Canales, Ignacio I. Wistuba, Adi Gazdar, Yang Xie, Guanghua Xia, "Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis", Journal of Thoracic Oncology, Volume 12, pp.501-509, March 2017.
  16. 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.
  17. 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]
  18. 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]
  19. Chao Li, Xinggang Wang, Wenyu Liu, Longin Jan Lateckib, Bo Wang and Junzhou Huang, "Weakly Supervised Mitosis Detection in Breast Histopathology Images Using Concentric Loss", Medical Image Analysis, Volume 53, pp.165-178, April 2019.
  20. Chaoqi Chen, Weiping Xie, Tingyang Xu, Wenbing Huang, Yu Rong, Xinghao Ding, Yue Huang and Junzhou Huang, “Progressive Feature Alignment for Unsupervised Domain Adaptation", In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR'19, Long Beach, CA, USA, June 2019.
  21. Yifan Zhang, Hanbo Chen, Ying Wei, Peilin Zhao, Jiezhang Cao, Mingkui Tan, Qingyao Wu, Xinjuan Fan, Xiaoying Lou, Hailing Liu, Jinlong Hou, Xiao Han, Jianhua Yao and Junzhou Huang, "From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification", In Proc. of the 21st Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'19, Shenzhen, China, October 2019.
  22. Hanbo Chen, Xiao Han, Xinjuan Fan, Xiaoying Lou, Hailing Liu, Junzhou Huang and Jianhua Yao, "Rectified Cross-Entropy and Upper Transition Loss for Weakly Supervised Whole Slide Image Classifier", In Proc. of the 21st Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'19, Shenzhen, China, October 2019.
  23. 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]
  24. 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.
  25. 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]

 


Figure 1. Image-Omics Data: Pathological Image, Gene Mutation, CNV, mRNA Expression, Protein Expression

 

Efficient Cell Detection in the Whole Slide Pathological Images

Figure 2. Cell detection in a whole slide image (13483 x 17943); Time: ~200 seconds; Desktop Computer: Nvidia Tesla K40c, Regular 5400 RPM HDD