Neural Networks
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision Support System for Multiuser Remote Microscopy in Telepathology
CBMS '99 Proceedings of the 12th IEEE Symposium on Computer-Based Medical Systems
Implementation of Kernel Methods on the GPU
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Stroma classification for neuroblastoma on graphics processors
International Journal of Data Mining and Bioinformatics
Cholangiocarcinoma--An Automated Preliminary Detection System Using MLP
Journal of Medical Systems
Degree prediction of malignancy in brain glioma using support vector machines
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Multicriteria fuzzy assignment method: a useful tool to assist medical diagnosis
Artificial Intelligence in Medicine
International Journal of High Performance Computing Applications
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In the present study a new strategy is introduced for designing and developing of an efficient dynamic Decision Support System (DSS) for supporting rare cancers decision making. The proposed DSS operates on a Graphics Processing Unit (GPU) and it is capable of adjusting its design in real time based on user-defined clinical questions in contrast to standard CPU implementations that are limited by processing and memory constrains. The core of the proposed DSS was a Probabilistic Neural Network classifier and was evaluated on 140 rare brain cancer cases, regarding its ability to predict tumors' malignancy, using a panel of 20 morphological and textural features Generalization was estimated using an external 10-fold cross-validation. The proposed GPU-based DSS achieved significantly higher training speed, outperforming the CPU-based system by a factor that ranged from 267 to 288 times. System design was optimized using a combination of 4 textural and morphological features with 78.6% overall accuracy, whereas system generalization was 73.8%+/-3.2%. By exploiting the inherently parallel architecture of a consumer level GPU, the proposed approach enables real time, optimal design of a DSS for any user-defined clinical question for improving diagnostic assessments, prognostic relevance and concordance rates for rare cancers in clinical practice.