Hi! I am Ke YAN, a postdoc researcher in National Institutes of Health, US. My tutors are Dr. Ronald Summers and Dr. Le Lu. My research interests include computer vision, madical image analysis, deep learning, and machine learning. I am using deep learning methods to tackle new and challenging problems in medical image analysis.
I came from from Beijing, China. I got my Ph.D. and BS degrees both from Dept. Electronic Engineering, Tsinghua University, China. My advisor is Prof. David Zhang, IEEE fellow. I am the winner of 2016 Tsinghua University Excellent Doctoral Dissertation Award.
E-mail: yankethu <at> gmail.com
My CV is here.
- [2018.09.17] I received the RSNA Trainee Research Prize with our submission "Relationship Learning and Organization of Significant Radiology Image Findings for Lesion Retrieval and Matching"!
- [2018.09.16] Invited talk at the MICCAI 2018 Workshop of Computational Precision Medicine in Granada, Spain.
- [2018.09.13] Invited talk at the NIH Research Festival, Symposium on Computational Biology, Data Science, and Machine Learning.
- [2018.07.20] The DeepLesion dataset was released! Multi-category dataset of 32K lesions on CT images. (Details)
- [2018.07.20] Our work of DeepLesion was reported by NIH, SPIE, AuntMinnie, and other news websites!
- [2018.06.23] I was invited to give a talk at the Medical Computer Vision and Health Informatics Workshop of CVPR 2018 at Salt Lake City, US. (slides)
- [2018.04.06] Our paper "Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks" was featured in the RSIP Vision and ISBI Daily in coorperation with Computer Vision News. (link)
Recent PapersGoogle Scholar
- Ke Yan et al., "Relationship Learning and Organization of Significant Radiology Image Findings for Lesion Retrieval and Matching" (Scientific Paper, RSNA Trainee Research Prize); "3D Context Enhanced Region-based Convolutional Neural Network for Universal Lesion Detection in a Large Database of 32,735 Manually Measured Lesions on Body CT" (Scientific Poster); Youbao Tang et al., "CT Image Enhancement for Lesion Segmentation Using Stacked Generative Adversarial Networks"; RSNA 2018, Chicago.
- Ke Yan, Mohammadhadi Bagheri, Ronald M. Summers, "3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection," International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), Granada, Spain, 2018 (arXiv, code)
- Jinzheng Cai*, Youbao Tang*, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers, "Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST", MICCAI, 2018 (arXiv)
- Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Mohammadhadi Bagheri, Ronald M. Summers, "DeepLesion: a Diverse and Large-scale Database of Significant Radiology Image Findings," MICCAI workshop-Large-scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS), 2018.
- Youbao Tang*, Jinzheng Cai*, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers, "CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement," MICCAI workshop-International Conference on Machine Learning in Medical Imaging (MLMI), 2018 (arXiv)
- Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam Harrison, Mohammadhadi Bagheri, Ronald M. Summers, "Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database," IEEE CVPR, 2018 (arXiv, dataset)
- Ke Yan, Le Lu, Ronald M. Summers, "Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks", IEEE International Symposium on Biomedical Imaging (ISBI), oral, 2018 (arXiv, code)
- Ke Yan*, Xiaosong Wang*, Le Lu, Ronald Summers, "Detection of Radiology Image Findings using Large-Scale Clinical Lesion Annotations", RSNA 2017, scientific poster (arXiv)
- Ke Yan and David Zhang, "Blood glucose prediction by breath analysis system with feature selection and model fusion," 2014 36th Annual Intl. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), oral presentation, Chicago, 2014.
- Ke Yan, Youbin Chen, and David Zhang, "Gabor surface feature for face recognition," 2011 First Asian Conference on Pattern Recognition (ACPR), oral presentation, 2011.
- Ke Yan, Xiaosong Wang, Le Lu, Ronald M. Summers, "DeepLesion: Automated Mining of Large-Scale Lesion Annotations and Universal Lesion Detection with Deep Learning", Journal of Medical Imaging, 2018 (paper)
- Ke Yan, Lu Kou, and David Zhang, "Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization,” IEEE Transactions on Cybernetics. (IF=4.943), Jan., 2017
- Ke Yan, Lu Kou, and David Zhang, “Correcting Instrumental Variation and Time-Varying Drift Using Parallel and Serial Multitask Learning,” IEEE Transactions on Instrumentation and Measurement (IF=2.456), Jun., 2017.
- Ke Yan, and David Zhang, “Correcting Instrumental Variation and Time-varying Drift: A Transfer Learning Approach with Autoencoders,” IEEE Transactions on Instrumentation and Measurement (IF=1.808), Sep., 2016.
- Ke Yan, and David Zhang, “Calibration transfer and drift compensation of e-noses via coupled task learning,” Sensors and Actuators B: Chemical (IF=4.097), Mar., 2016.
- Ke Yan, and David Zhang, “Improving the transfer ability of prediction models for electronic noses,” Sensors and Actuators B: Chemical (IF=4.097), Dec., 2015.
- Ke Yan, and David Zhang, “Feature selection and analysis on correlated gas sensor data with recursive feature elimination,” Sensors and Actuators B: Chemical (IF=4.097), Jun., 2015.
- Ke Yan, David Zhang, Darong Wu, Hua Wei, and Guangming Lu, “Design of a breath analysis system for diabetes screening and blood glucose level prediction,” IEEE Transactions on Biomedical Engineering (IF=2.347), Nov., 2014.
- David Zhang, Dongmin Guo, and Ke Yan, “Breath Analysis for Medical Applications,” Springer, 2017.
Education and working experiences
- 2017.01- : PostDoc in Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, US. Current research direction: lesion detection and characterization in CT images with deep learning.
- 2016.08-2016.11 : Researcher in Deephi Tech. Topic: Pedestrian detection using convolutional neural networks.
- 2011.09-2016.07 : PhD student in Dept. Electronic Engineering, Tsinghua University.
- 2015.07-2015.08 : Intern in IBM China Research Lab. Topic: Robot-based intelligent shopping assistant.
- 2013.07-2013.08 : Intern in XingKe Intelligent Tech. Co. Ltd., China. Topic: Gesture recognition based on Kinect and Unity3D.
- 2011.08-2012.08 : Research Assistant in Dept. Computing, Hong Kong Polytechnic University.
- 2010.03-2011.08 : Research topic: A face recognition system, received over 10k downloads until 2014 in Google code.
- 2006.09-2010.07 : Undergraduate student in Dept. Electronics Engineering, Tsinghua University.
- Tsinghua University Excellent Doctoral Dissertation Award (2016);
- First prize of Tsinghua Outstanding Scholarship, 2 times (2014, 2015);
- First prize of Foxconn Scholarship, 2 times (2012, 2013);
- Best Intern Demonstration in IBM China Research Lab (2015);
- Best Creativity Award in the First Photo Contest of University Town of Shenzhen.