We present a pathological image analysis system for the computer-aided prognosis of neuroblastoma, a childhood cancer. The image analysis system automatically classifies Schwannian stromal development of pathological tissues and determines the grade of differentiation. Due to the demanding computational cost of processing large digitized slides, the system was implemented on a cluster of computers with automated load balancing within a multi-resolution framework. In our experiments, the overall accuracies for stromal classification and the grade of differentiation were 96.6% and 95.3%, respectively. Additionally, the multi-resolution framework reduced the run time of the single resolution approach by 53% and 34% on average for stromal classification and grade of differentiation, respectively. For these two cases, parallelization on a 16-node cluster reduced the sequential run time by 92% and 88% on average. Accuracy and efficiency of these techniques are promising for the development a computer-assisted neuroblastoma prognosis system
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