Int J Comput Assist Radiol Surg - Multimodality GPU-based computer-assisted diagnosis of breast cancer using ultrasound and digital mammography images.

Tópicos

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Resumo

RPOSE: To improve the computer-aided diagnosis of breast lesions, by designing a pattern recognition system (PR-system) on commercial graphics processing unit (GPU) cards using parallel programming and textural information from multimodality imaging.MATERIAL AND METHODS: Patients with histologically verified breast lesions underwent both ultrasound (US) and digital mammography (DM), lesions were outlined on the images by an experienced radiologist, and textural features were calculated. The PR-system was designed to provide highest possible precision by programming in parallel the multiprocessors of the NVIDIA's GPU cards, GeForce 8800GT or 580GTX, and using the CUDA programming framework and C++. The PR-system was built around the probabilistic neural network classifier, and its performance was evaluated by a re-substitution method, for estimating the system's highest accuracy, and by the external cross-validation method, for assessing the PR-system's unbiased accuracy to new, "unseen" by the system, data.RESULTS: Classification accuracies for discriminating malignant from benign lesions were as follows: 85.5 % using US-features alone, 82.3 % employing DM features alone, and 93.5 % combining US and DM features. Mean accuracy to new "unseen" data for the combined US and DM features was 81 %. Those classification accuracies were about 10 % higher than accuracies achieved on a single CPU, using sequential programming methods, and 150-fold faster.CONCLUSION: The proposed PR-system improves breast-lesion discrimination accuracy, it may be redesigned on site when new verified data are incorporated in its depository, and it may serve as a second opinion tool in a clinical environment.

Resumo Limpo

rpose improv computeraid diagnosi breast lesion design pattern recognit system prsystem commerci graphic process unit gpu card use parallel program textur inform multimod imagingmateri method patient histolog verifi breast lesion underw ultrasound us digit mammographi dm lesion outlin imag experienc radiologist textur featur calcul prsystem design provid highest possibl precis program parallel multiprocessor nvidia gpu card geforc gt gtx use cuda program framework c prsystem built around probabilist neural network classifi perform evalu resubstitut method estim system highest accuraci extern crossvalid method assess prsystem unbias accuraci new unseen system dataresult classif accuraci discrimin malign benign lesion follow use usfeatur alon employ dm featur alon combin us dm featur mean accuraci new unseen data combin us dm featur classif accuraci higher accuraci achiev singl cpu use sequenti program method fold fasterconclus propos prsystem improv breastles discrimin accuraci may redesign site new verifi data incorpor depositori may serv second opinion tool clinic environ

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