Workshop, University 1, Department, 2015
Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning.
Workshop, University 1, Department, 2015
The machine learning track seeks novel contributions that address current methodological gaps in analyzing high-dimensional, longitudinal, and heterogeneous clinical neuroimaging data using stable, scalable, and interpretable machine learning models, include Spatio-temporal brain data analysis,Model scalability in large neuroimaging datasets,Unsupervised methods for stratifying brain disorders,Deep learning in clinical neuroimaging,Model uncertainty in clinical predictions. In the clinical neuroimaging track, we seek applications of existing machine learning approaches to address major challenges towards reaching precision medicine for brain disorders, e.g., Biomarker discovery,Biological validation of clinical syndromes and Brain aging.
Workshop, University 1, Department, 2015
We focused on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans.Tumor segmentation provides a milestone for determination of the exact tumor size for computer aided diagnosis (CAD). Liver lesion segmentation is a significant step in liver cancer diagnosis, treatment planning and treatment evaluation. Liver Tumor Segmentation Challenge (LiTS) offers a common testbed for comparing different automated liver lesion segmentation techniques. The main use of digital image processing is to increase the quality of images for interpretation of human and machine understanding. Tumor segmentation in liver computed tomography (CT) volumes is considered as a complex task because of the varying tumor shape and texture. The location of the tumor also presents a challenge. Manual segmentation of tumors is a time-consuming task that can be inaccurate in some cases. The aim of this research is to propose an automated method, which can detect the tumor in each slice in volumetric CT liver images.
Undergraduate course, University 1, Department, 2014
After decades of active research using in-vivo functional neuroimaging techniques such as fMRI, there has been mounting evidence that the total human brain function emerges from and is realized by the interaction of multiple concurrent neural networks. However, due to the lack of effective computational brain mapping approaches and the limitation of functional neuroimaging data quality/quantity, it is still challenging to robustly and faithfully reconstruct concurrent functional networks from fMRI (either task fMRI (tfMRI) or resting state fMRI (rsfMRI)) data and quantitatively measure their network-level interactions. Thus, it is largely unknown to what extent those multiple interacting functional networks spatially overlap with each other and jointly realize the total brain function. In response, recently, by developing innovative sparse representation of whole-brain fMRI signals and by using the publicly released large-scale Human Connectome Project (HCP) high-quality fMRI data, our pilot studies have shown that a large number of reproducible and robust functional networks, including both task-evoked and resting state networks, are simultaneously distributed in distant neuroanatomic areas while substantially spatially overlapping with each other, thus forming an initial collection of holistic atlases of functional networks and interactions