a b s t r a c tWe propose a new procedure for quantitative evaluation of object detection algorithms. The procedure consists of a matching stage for finding correspondences between reference and output objects, an accuracy score that is sensitive to object shapes as well as boundary and fragmentation errors, and a ranking step for final ordering of the algorithms using multiple performance indicators. The procedure is illustrated on a building detection task where the resulting rankings are consistent with the visual inspection of the detection maps.
Abstract-We describe an image representation that combines the representational power of graphs with the efficiency of the bag-of-words model. For each image in a data set, first, a graph is constructed from local patches of interest regions and their spatial arrangements. Then, each graph is represented with a histogram of subgraphs selected using a frequent subgraph mining algorithm in the whole data. Using the subgraphs as the visual words of the bag-of-words model and transforming of the graphs into a vector space using this model enables statistical classification of images using support vector machines. Experiments using images cut from a large satellite scene show the effectiveness of the proposed representation in classification of complex types of scenes into eight high-level semantic classes.
The high tumor heterogeneity makes it very challenging to identify key tumorigenic pathways as therapeutic targets. The integration of multiple omics data is a promising approach to identify driving regulatory networks in patient subgroups. Here, we propose a novel conceptual framework to discover patterns of miRNA-gene networks, observed frequently up- or down-regulated in a group of patients and to use such networks for patient stratification in hepatocellular carcinoma (HCC). We developed an integrative subgraph mining approach, called iSubgraph, and identified altered regulatory networks frequently observed in HCC patients. The miRNA and gene expression profiles were jointly analyzed in a graph structure. We defined a method to transform microarray data into graph representation that encodes miRNA and gene expression levels and the interactions between them as well. The iSubgraph algorithm was capable to detect cooperative regulation of miRNAs and genes even if it occurred only in some patients. Next, the miRNA-mRNA modules were used in an unsupervised class prediction model to discover HCC subgroups via patient clustering by mixture models. The robustness analysis of the mixture model showed that the class predictions are highly stable. Moreover, the Kaplan-Meier survival analysis revealed that the HCC subgroups identified by the algorithm have different survival characteristics. The pathway analyses of the miRNA-mRNA co-modules identified by the algorithm demonstrate key roles of Myc, E2F1, let-7, TGFB1, TNF and EGFR in HCC subgroups. Thus, our method can integrate various omics data derived from different platforms and with different dynamic scales to better define molecular tumor subtypes. iSubgraph is available as MATLAB code at http://www.cs.umd.edu/~ozdemir/isubgraph/.
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