I am currently a third year computer science Ph.D. student from UT Arlington. My supervisor is Prof. Junzhou Huang . Generally, I am passionate at applying machine learning algorithms and creating new models to solve challenges met with the development of bioinformatics and biomedicine. I have special interests in developing practical algorithms to mine useful information from Giga-pixel histopathological images. I enjoy and am experienced at cooperating with experts from medicine and other domains to solve interdisciplinary problems. Before coming to UT Arlington, I spent 3 years in Institute of Automation, Chinese Academy of Sciences (CASIA). In CASIA, I built and tested intelligent algorithms for miniature autonomous vehicle.
Research Interests: Deep Learning, Machine Learning, Bioinformatics, Medical Image Processing, Computer Vision
- 03/2017 One paper accepted by CVPR 2017
- 11/2016 Awarded IEEE BIBM 2016 Student Travel Award
- 10/2016 Two papers accpeted by BIBM 2016
- 05/2016 One paper accepted by MICCAI 2016
- 12/2015 One paper accepted by ISBI 2016
Xinliang Zhu, Feiyun Zhu and Junzhou Huang. Deep Correlational Learning for Survival Prediction from Multi-modality Data , In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 406-414. Springer, Cham, 2017. (Oral)
Xinliang Zhu, Jiawen Yao, Feiyun Zhu and Junzhou Huang. WSISA: Making Survival Prediction from Whole Slide Pathology Images , In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7234-7242. 2017.
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 Bioinformatics and Biomedicine, 2016 IEEE International Conference on, pp. 455-459. IEEE, 2016. (BIBM) 2016. (Regular Paper: acceptance rate ~19%)
Xinliang Zhu, Jiawen Yao and Junzhou Huang. Deep Convolutional Neural Network for Survival Analysis with Pathological Images , IEEE International Conference on Bioinformatics and Biomedicine, 2016 IEEE International Conference on, pp. 544-547. IEEE, 2016. (BIBM) 2016. ( Short Paper: acceptance rate ~19% )
Jiawen Yao, Sheng Wang, Xinliang Zhu and Junzhou Huang. Imaging Biomarker Discovery for Lung Cancer Survival Prediction , International Conference on Medical Image Computing and Computer-Assisted Intervention: 2016 (MICCAI), 649--657, 2016. (Oral)
Xinliang Zhu, Jiawen 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 , 2016 IEEE 13th International Symposium on (ISBI), 1173-1176, 2016.
Feiyun Zhu, Bin Fan, Xinliang Zhu, Ying Wang, Shiming Xiang and Chunhong Pan. 10,000+ Times Accelerated Robust Subset Selection (ARSS), Proc. Assoc. Adv. Artif. Intell 2015 (AAAI).
- One deep attention-based model was proposed to solve the challenging problem. Very promising results were achieved on two types of cancers datasets;
- We are trying to generalize our model to solve other fine-grained learning problems with weak labels.
- Efficient deep learning models with specific training strategies were developed to solve the first two problems;
- A novel framework was also created to solve the third challenge.
- Integrating features extracted from pathology image patches with genetic signature expressions to improve the survival prediction accuracy;
- Developing methods for imaging biomarker discovery;
- Mapping clinical outcome correlated Imaging-Genetic data by developing supervised conditional Gaussian graphical model (SuperCGGM).
- Created a novel method for RGB and depth images registration in outdoor scenes;
- Collected over 10,000 RGBD outdoor pedestrain RGBD images;
- Developed a fast pedestrian detection framework based on RGBD images.
Large Scale Learning for Complex Image-Omics Data Analytics
This project aims to develop computational tools for analyzing complex pathology image data as well genomics data. To solve the key and challenging problems in mining comprehensive heterogeneous image and genomic data, novel large scale learning tools and explore ways to integrate features from multiple data sources for clinical outcome prediction are developed. It will greatly support the Precision Medicine Initiative, which enables physicians to select individualized treatments. I work on developing feature learning from gigapixel whole slide pathology images and integrating imaging-omics data methods.
Miniature Autonomous Vehicle
The miniature autonomous vehicle was built from a toy car. It mainly consisted of a toy car, 3 web cameras, 1 ultrasonic radar and a motherboard equipped with Ubuntu 12.04LTS. It could do traffic signs recognition, traffic light recognition, road line detection and also be with the basic functions of a toy car. I was in charge of building the traffic signs recognition module and testing the hardware & software architecture.
SOAR-based Air Traffic Control Simulation System
Using SOAR as a cognition architecture to simulate an air traffic controller’s decision making could potentially make the training of an air traffic controller easier and with more fun. I was the software architect of the whole system and the main designer of the rules used in SOAR architecture