Stochastic simulation
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
Basic Concepts and Taxonomy of Dependable and Secure Computing
IEEE Transactions on Dependable and Secure Computing
Methods and Procedures for the Verification and Validation of Artificial Neural Networks
Methods and Procedures for the Verification and Validation of Artificial Neural Networks
Contract-based justification for COTS component within safety-critical applications
SCS '04 Proceedings of the 9th Australian workshop on Safety critical systems and software - Volume 47
Guidance for the Verification and Validation of Neural Networks (Emerging Technologies)
Guidance for the Verification and Validation of Neural Networks (Emerging Technologies)
Software architecture reliability analysis using failure scenarios
Journal of Systems and Software
Analysis of mammography reports using maximum variation sampling
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A genetic algorithm for learning significant phrase patterns in radiology reports
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
| Hi-index | 0.00 |
Recent advances in techniques such as image analysis, text analysis and machine learning have shown great potential to assist physicians in detecting and diagnosing health issues in patients. In this paper, we describe the approach and findings of an architecture-level dependability analysis for a mammography decision support system that incorporates these techniques. The goal of the research described in this paper is to provide an initial understanding of the dependability issues, particularly the potential failure modes and severity, in order to identify areas of potential high risk. The results will guide design decisions and provide the basis of a dependability and performance evaluation program.