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We present a framework for identifying disease states by classifying cells in the pathological regions of tissues into different categories. We use conditional random fields (CRF) to incorporate characteristics of cells and their spatial distributions. The efficacy of CRF to model cell-cell feature interactions is demonstrated by using a lung tissue dataset and a synthesized cancer tissue dataset...
The performance of automated analysis of cellular images is heavily influenced by the features that characterize cells or cell nuclei. In this paper, an exhaustive set of features including morphological, topological, and texture features are explored to determine the optimal features for classification of cells and cell nuclei. The optimal subset of features are obtained using popular feature selection...
There has been an increasing interest recently in identifying subcellular proteins from cellular images in order to understachind subcellular activities of cells. However, accuracies of the prediction tend to decrease with the number of protein subcellular localization classes. Therefore in this paper, we introduce a multiple-cell model with a higher-order Markov random fields (MRF) to combine predictions...
During the past few years, various traffic forecasting models have been developed to monitor traffic in network. However, these strategies rest on the assumption that the pattern that has been identified will continue into the future. Such these strategies cannot be expected to give good predictions unless this assumption is valid. In this paper, we combine an ARIMA model and SVM model for obtaining...
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