2 research outputs found
Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network
With the arrival of the big data era, more and more data are becoming readily
available in various real-world applications and those data are usually highly
heterogeneous. Taking computational medicine as an example, we have both
Electronic Health Records (EHR) and medical images for each patient. For
complicated diseases such as Parkinson's and Alzheimer's, both EHR and
neuroimaging information are very important for disease understanding because
they contain complementary aspects of the disease. However, EHR and neuroimage
are completely different. So far the existing research has been mainly focusing
on one of them. In this paper, we proposed a framework, Memory-Based Graph
Convolution Network (MemGCN), to perform integrative analysis with such
multi-modal data. Specifically, GCN is used to extract useful information from
the patients' neuroimages. The information contained in the patient EHRs before
the acquisition of each brain image is captured by a memory network because of
its sequential nature. The information contained in each brain image is
combined with the information read out from the memory network to infer the
disease state at the image acquisition timestamp. To further enhance the
analytical power of MemGCN, we also designed a multi-hop strategy that allows
multiple reading and updating on the memory can be performed at each iteration.
We conduct experiments using the patient data from the Parkinson's Progression
Markers Initiative (PPMI) with the task of classification of Parkinson's
Disease (PD) cases versus controls. We demonstrate that superior classification
performance can be achieved with our proposed framework, comparing with
existing approaches involving a single type of data
Towards in-store multi-person tracking using head detection and track heatmaps
Computer vision algorithms are being implemented across a breadth of
industries to enable technological innovations. In this paper, we study the
problem of computer vision based customer tracking in retail industry. To this
end, we introduce a dataset collected from a camera in an office environment
where participants mimic various behaviors of customers in a supermarket. In
addition, we describe an illustrative example of the use of this dataset for
tracking participants based on a head tracking model in an effort to minimize
errors due to occlusion. Furthermore, we propose a model for recognizing
customers and staff based on their movement patterns. The model is evaluated
using a real-world dataset collected in a supermarket over a 24-hour period
that achieves 98% accuracy during training and 93% accuracy during evaluation