BACKGROUND: Biomedical information and knowledge, structural and non-structural, stored in different repositories can be semantically connected to form a hybrid knowledge network. How to compute relatedness between concepts and discover valuable but implicit information or knowledge from it effectively and efficiently is of paramount importance for precision medicine, and a major challenge facing the biomedical research community. RESULTS: In this study, a hybrid biomedical knowledge network is constructed by linking concepts across multiple biomedical ontologies as well as non-structural biomedical knowledge sources. To discover implicit relatedness between concepts in ontologies for which potentially valuable relationships (implicit knowledge) may exist, we developed a Multi-Ontology Relatedness Model (MORM) within the knowledge network, for which a relatedness network (RN) is defined and computed across multiple ontologies using a formal inference mechanism of set-theoretic operations. Semantic constraints are designed and implemented to prune the search space of the relatedness network. CONCLUSIONS: Experiments to test examples of several biomedical applications have been carried out, and the evaluation of the results showed an encouraging potential of the proposed approach to biomedical knowledge discovery.

Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made to uncover implicit knowledge within large biomedical ontologies by exploring semantic similarity and relatedness between concepts. However, much less attention has been paid to another potentially helpful approach: discovering implicit knowledge across multiple ontologies of different types, such as disease ontologies, symptom ontologies, and gene ontologies. In this paper, we propose a unified approach to the problem of ontology based implicit knowledge discovery - a Multi-Ontology Relatedness Model (MORM), which includes the formation of multiple related ontologies, a relatedness network and a formal inference mechanism based on set-theoretic operations. Experiments for biomedical applications have been carried out, and preliminary results show the potential value of the proposed approach for biomedical knowledge discovery.

Video event classification and retrieval has recently emerged as a challenging research topic. In addition to the variation in appearance of visual content and the large scale of the collections to be analyzed, this domain presents new and unique challenges in the modeling of the explicit temporal structure and implicit temporal trends of content within the video events. In this study, we present a technique for video event classification that captures temporal information over semantics using a scalable and efficient modeling scheme. An architecture for partitioning videos into a linear temporal pyramid, using segments of equal length and segments determined by the patterns of the underlying data, is applied over a rich underlying semantic description at the frame level using a taxonomy of nearly 1000 concepts containing 500,000 training images. Forward model selection with data bagging is used to prune the space of temporal features and data for efficiency. The system is implemented in the Hadoop Map-Reduce environment for arbitrary scalability. Our method is applied to the TRECVID Multimedia Event Detection 2012 task. Results demonstrate a significant boost in performance of over 50%, in terms of mean average precision, compared to common max or average pooling, and 17.7% compared to more complex pooling strategies that ignore temporal content.

In this paper, we develop a new method for feature selection and category learning. We first introduce two observations from our experiments: (1) It is easier to distinguish two concepts than to learn an isolated concept. (2) To distinguish different concept pairs we can find different selections of optimal features. These two observations may partly explain the success of human vision learning, especially why an infant can simultaneously capture distinguished visual features when learning new concepts. Based on these two observations, we developed a new learning-by-focusing method which first constructs focalized concept discriminators for pairs of concepts, and then builds nonlinear classifiers using the discrimination scores. We build datasets for four concept structure: vehicle, human affliction, sports, and animals, and experiments on all the four datasets verify the success of our new approach.

In this study, we present a system for video event classification that generates a temporal pyramid of static visual semantics using minimum-value, maximum-value, and average-value aggregation techniques. Kernel optimization and model subspace boosting are then applied to customize the pyramid for each event. SVM models are independently trained for each level in the pyramid using kernel selection according to 3-fold cross-validation. Kernels that both enforce static temporal order and permit temporal alignment are evaluated. Model subspace boosting is used to select the best combination of pyramid levels and aggregation techniques for each event. The NIST TRECVID Multimedia Event Detection (MED) 2011 dataset was used for evaluation. Results demonstrate that kernel optimizations using both temporally static and dynamic kernels together achieves better performance than any one particular method alone. In addition, model sub-space boosting reduces the size of the model by 80%, while maintaining 96% of the performance gain.

Automatic recognition or classification of medical image modality can provide valuable information for medical image retrieval and analysis. In this paper, we discuss an application of SVM ensemble classifiers to the problem, and explore a confidence indicator based late fusion method to resolve ambiguity across competing classes. Using a matrix of closeness and a set of additional fusion rules, the proposed method improves the classification performance by only subjecting likely misclassified samples to a text-based classifier followed by additional fusion of both image-based classification and text-based classification results. An empirical evaluation using standard ImageClef2010 Medical Retrieval data show very promising performance for the proposed approach.

Registration with an atlas is a method to recognize and label structures that is used especially for the analysis of medical images. Its application to images containing many objects, such as images of the human abdomen, may prove to be very challenging. The underlying idea of our approach is to follow a strategy that resembles commonsense problem solving - goal-directed, context-based and focused. In our method, the goal of recognizing a particular anatomical structure, such as the aorta, is achieved by following a plan, which is a sequence of image registration tasks. The plan is derived by decomposing the registration of the goal object into a series of object registration subtasks using the anatomy knowledge and the modality-specific image properties. Preliminary experiments to recognize the aorta in human abdominal CT images have demonstrated the potential of our method.

Medical imaging has made great technological breakthroughs in multimodal acquisition, visualization, and analysis with many complementary image modalities to non-invasively capture human anatomy, physiology and pathology. New functional imaging techniques help elucidate the dynamics of human health and disease at much higher throughput, enabled by growth in affordable computing power, memory capacity, processor speed, and communication bandwidth. Recently, exceedingly powerful computer hardware and optimized image processing software may, for the first time, allow high volume image data processing and manipulation (like multimodal registration) to become clinically feasible on a routine basis in real-time. In reviewing recent medical imaging advances, we discuss some major open issues and directions.

Emerging multi-core processors are able to accelerate medical imaging applications by exploiting the parallelism available in their algorithms. We have implemented a mutual-information-based 3D linear registration algorithm on the Cell Broadband Enginetrade processor. By exploiting the highly parallel architecture and its high memory bandwidth, our implementation with two CBE processors can register a pair of 256x256x30 3D images in one second. This implementation is significantly faster than a conventional one on a traditional microprocessor or even faster than a previously reported custom-hardware implementation. In addition to parallelizing the code for multiple cores and organizing the data structure for reducing the amount of the memory traffic, it is also critical to optimize the code for the SIMD pipeline structure. We note that code optimization for the SIMD pipeline alone results in a 4.2x-8.7x acceleration for the computation of small kernels. Further, SIMD optimization alone results in a 4.5x end-end application speedup.

There have been increasingly growing interests in computational modeling of contextual knowledge representation and context-based problem solving. Different approaches have been developed to exploring the concept and computational mechanism of context, including various formal and empirical methods and systems based on different interpretations and applications of the notion. This paper discusses two basic interpretations of context and presents an operational definition of context from a computational perspective. Two paradigms for context-based problem solving are formulated. A goal-directed modeling framework for context-based problem solving is reported. In this framework, a context is defined in terms of an object and its relationships with other objects. Every context is centered at such an object, and cannot exist without it. A problem-solving task (e.g. find an object) with this framework can thus be defined as a process of determining a context or a sequence of contexts in which a solution path (e.g. steps for finding the object) can be decided. A set of operations is specified for context manipulation. Some application examples are described.

Web-based customer service has become a norm of business practice with increasing emphasis on modeling customer needs and providing them with targeted or personalized service solutions in a timely fashion. Almost all the commercial Web service systems adopt some kind of simple customer segmentation models and shallow pattern matching or rule-based techniques for high performance. The models built based on these techniques though very efficient have a fundamental limitation in their ability to capture and explain the reasoning in the process of determining and selecting appropriate services or products. However, using deep models (e.g. semantic networks), though desirable for their expressive power, may require significantly more computational resources (e.g. time) for reasoning. This can compromise the system performance. We report on a new approach that represents and uses contextual information in semantic net-based models to constrain and prune potentially very large search space, which results in much improved performance in terms of speed and selectivity as evidenced by the evaluation results.

Most human commonsense problem solving is done within a context, which constrains the solution space, whether it involves perception and image interpretation or not. Yet most research in image analysis still assumes context is defined a-priori by the investigator and is external to the computational image analysis system. Where explicit focus of attention and spatial contexts are used, as in active vision systems, these are problem-specific. We report on a new approach to image analysis, which includes a general framework for defining spatial context in terms of reference objects and their spatial relationships to other objects in a scene. Image analysis problems are decomposed into a sequence of sub-problems corresponding to determining a sequence of spatial contexts based on the set of dynamically chosen reference objects. The experimental results with medical image analysis and interpretation have demonstrated that using reference objects to define spatial contexts is a very effective strategy for computer image analysis systems that can purposely focus the image analysis effort on the most promising part of the entire image, such that the target object can be quickly and accurately localized by eliminating most potential false positives.

The potential of lossy image compression is investigated in a well defined task domain, specifically, clinical assessment of chromatic, surgical and hematopathology specimens. Two criteria were employed, just noticeable difference and largest tolerable distortion. Compression tolerances differed among pathologists, but conformed to well defined upper and lower limits. The level of tolerable compression was significantly larger for the second criterion. It is concluded that lossy image compression is feasible for diagnostic pathology and may hold promise for telepathology applications.

This paper presents a new approach to the knowledge-based composition of processes for image interpretation and analysis. Its computer implementation in the VISIPLAN (Vision Planner) system provides a way of modeling the composition of image analysis processes within a unified, object-centered hierarchical planning framework. The approach has been tested and shown to be flexible in handling a reasonably broad class of multi-modality biomedical image analysis and interpretation problems. It provides a relatively general design or planning framework, within which problem specific image analysis and recognition processes can be generated more efficiently and effectively. In this way, generality is gained at the design and planning stages, even though the final implementation stage of interpretation processes is almost invariably problem- and domain-specific.>

This paper presents a new knowledge-based approach to the composition of biomedical image analysis processes. A prototype system has been developed that provides the imaging domain expert with the tools needed to formulate, specify and experiment with the design of analysis strategies. The system has been tested in the area of MR image analysis with promising results.>