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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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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.
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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.
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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.
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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.
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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)
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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.
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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.
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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.
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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)
- 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.
- 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.
- 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.
- 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)
- 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.
- 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.
- 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.
- 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]
- 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]
- 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.
- 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.
- 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.
- 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.
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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]
- 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.
- 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]
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Figure
1. Image-Omics Data: Pathological Image, Gene Mutation, CNV,
mRNA Expression, Protein Expression
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Efficient Cell Detection in the Whole Slide
Pathological Images
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Figure
2. Cell detection in a whole slide image (13483 x 17943);
Time: ~200 seconds; Desktop Computer: Nvidia Tesla K40c, Regular
5400 RPM HDD
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