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Advances in medical imaging techniques and devices has resulted in increased use of imaging in monitoring disease progression in patients. However, extracting decision-enabling information from the resulting longitudinal multi-modal image sets poses a challenge. Radiologists often have to manually identify and quantify certain regions of interest in the longitudinal image sets, which bear upon the...
Monitoring multimodal data generated by sensor networks for extracting information is a challenging task for the human observer. To manage the barrage of data, one needs to create mechanisms for identifying only those time intervals which are informative and worthy of further highlevel analysis either by machine or the human observer. We regard a time interval to be informative and contain an event...
In this paper we propose a novel transductive learning machine for spatiotemporal classification casted as an interactive segmentation problem. We present Markov conditional mixtures of naive Bayes models with spatiotemporal regularization constraints in a transductive learning and inference framework. The proposed model extends on previous work to account for non independent and identically distributed...
We describe a platform for performing text and radiology analytics (TARA). We integrate commercially available hardware and middleware components to construct an environment which is well-suited for performing computationally intensive analyses of medical data. The system, termed a "medical analytical platform," adopts a client-server approach for the display and processing of radiology...
Current tools for contact centers provide simple mechanisms for manually logging a technical problem, and recording the route of the problem, from agent to agent, until a solution is identified and then recorded in a "problem ticket." These tools fall far short of accurately capturing the intricacies of technical problems, or providing the details of solution procedures to the right people...
Rapid advances in mobile computing devices and sensor technologies are enabling the capture of unprecedented volumes of data by individuals involved in field operations in a variety of applications. As capture becomes ever more rich and pervasive the biggest challenge is in developing information processing and representation tools that maximize the utility of the captured multi-sensory data. The...
A novel framework is introduced for visual event detection. Visual events are viewed as stochastic temporal processes in the semantic concept space. In this concept-centered approach to visual event modeling, the dynamic pattern of an event is modeled through the collective evolution patterns of the individual semantic concepts in the course of the visual event. Video clips containing different events...
We present novel algorithms for detecting generic visual events from video. Target event models will produce binary decisions on each shot about classes of events involving object actions and their interactions with the scene, such as airplane taking off, exiting car, riot. While event detection has been studied in scenarios with strong scene and imaging assumptions, the detection of generic visual...
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