Associate ProfessorXiu Wang

Panoromic data analysis and fusion 

• Detection and Treatment Outcome Prediction of Malignant Tumors and Cancers – Recently the research community has seen great success using deep learning for image analysis tasks. For example, the Convolutional Neural Network (CNN) is one of the most widely used methods for object detection/recognition. This project will use a deep learning approach for predictive, prognostic prediction of patient treatment outcomes of malignant brain tumors. The multi-layer convolution of CNN will be utilized for detection and segmentation of tumors/lesions, and the project will include the investigation on the effective features for training and design of more feasible deep learning scheme for treatment outcomes prediction.
• Collaborative Learning of Multimodality Medical Imaging Data – Comprehensive and possibly complementary information embedded in the multimodality images is of great importance for accurate clinical decision-making. Our project aims to develop collaborative learning algorithms to incorporate important features from multimodality images with prior domain knowledge optimal ultimate solutions to automated subject identification and segmentation.
• Content-oriented deformable image registration - Image registration is a fundamental image processing technique, which spatially registers images that may be obtained from multiple sensors, at different times, or at different view angles. Current image registration methods are either based on image intensity information or based on the extracted feature to derive deformation field. These registration schemas may introduce excessive deformation onto the local important regions, in which the local features are expected to be preserved. In our research, we will focus on derive deformation fields according to the image contents for more meaningful and sensible registration. We will use temporal medical images as testing data for our research.
• Fusion and Analytics of Omics Data - Clinical testing and assessment in adjunction with diverse medical imaging data are the basis for more effective patient treatment and management. However, how to fully utilize of the widely-available omics data including genomics, clinical characteristics, systemic immunity and radiomics is yet to investigate for individualized treatment. This project aims to analyze these multi-stream life data with visual analytics techniques and transform these analyses to our understanding of human health and disease pattern for prognostic medicine.