News!

• June, 2019 -- Our paper "3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI " is accepted by International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019. 

• Dec, 2018 -- Our deep learning for remote sensing paper "Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery " is accepted to be published in Remote Sensing. 

• July, 2018 -- Our new paper "Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images " is accepted to be published in Sensors . 

• May, 2017 -- I joined OMNI AI, INC in May 2017 as a senior scientist, working on object detection, tracking and anomaly detection with state-of-the-art deep learning methods. 

• Jan, 2017 -- I will attend the SPIE Medical Imaging 2017 in Orlando, Florida this February and present our work 'Local-Global Classifier Fusion for Screening Chest Radiographs'.

• May, 2016 -- Call for papers:  Recent Advances in Audio and Image Based HCI on Mobile Devices - Special Issue on Advances in Human-Computer Interaction 

More details can be found here: https://www.hindawi.com/journals/ahci/si/786456/cfp/

Bio

Dr. Meng Ding is currently working on computer vision and deep learning at Thermo Fisher Scientific. Before that, he was a senior scientist at OMNI AI, INC (Houston, Texas), working on real-time object detection using deep learning methods. Previously, he worked on computer vision and medical image analysis in the Lister Hill National Center for Biomedical Communications (LHNCBC), National Library of Medicine (NLM), National Institutes of Health (NIH), USA. He received his PhD degree in Electrical and Computer Engineering from the Oklahoma State University, USA, affiliated with VCIPL Group and supervised by Professor Guoliang Fan. He received his Master degree in  Optical Engineering (focusing on Augmented Reality) and the Bachelor degree (with honor) in Electrical Engineering from the Beijing Institute of Technology (北京理工大学) in 2009 and 2007 respectively.

PhD Dissertation

"Human Motion Analysis: From Gait Modeling to Shape Representation and Pose Estimation", summer 2015. [PDF]

Research Interests

• Computer Vision - Semantic Segmentation and 3D Volumetric Data Segmentation

• Medical Image Analysis - Segmentation in CT and MRI

• Object Detection and Tracking

• Human Pose Estimation

• Augmented Reality and Human-computer Interaction (HCI)

I am working at Bayer HealthCare - Radiology Division, focusing on medical image analysis and computer-aided diagnosis (CAD) using computer vision and deep learning algorithms, including 3D anatomical structure localization and segmentation from CT data. Previously, I worked on object detection, tracking and anomaly detection in OMNI AI, INC. Before that, I focused on image segmentation, detection and classifier fusion with chest X-ray images in the Lister Hill National Center for Biomedical Communications (LHNCBC), National Library of Medicine (NLM), National Institutes of Health (NIH), USA. During my PhD study, I mainly concentrated on the vision-based human motion analysis, which aims to estimate the human pose and analyze the human motion from the RGB camera or RGB-D sensor (e.g. Kinect). I also worked on hand pose estimation from the Kinect sensor and skeleton reconstruction with inertial sensors.

Academic Activities

Reviewer for Journals:

• IEEE Trans. on Image Processing (TIP),

• IEEE Trans. on Cybernetics,

• IEEE Trans. on Medical Imaging (TMI), 

• IEEE Journal of Biomedical and Health Informatics,

• IEEE Trans. on Multimedia

• CVIU,

• Image & Visual Computing, 

• Journal of Real-time Image Processing

• Sensors

• IET Computer Vision

• Multimidia Tools and Applications

• International Journal of Electronics and Communications

Reviewer for Conferences: 

• CVPR 2020

• AAAI 2019

• WACV 2019, 2018, 2017, 2015,

• FG 2018, 2017, 2015

• ICIP 2017

• MICCAI 2017, 2019

Technical Program Committee: 

• CRV 2017

• ICIVC 2017

PhD Forum in WACV 2015

Doctoral Consortium in FG 2013

IEEE Membership 2012 -- 2017

SPIE Membership 2017