This study of data pooled from 5 mammography registries found that computer-aided detection (CAD) does not improve diagnostic accuracy of mammography, and
After exhaustive experimentation of the methods proposed in this study, the results obtained are detailed in this section. The result presentation is sectioned into two major parts: firstly, the performance of the IEOSA metaheuristic algorithm, as evaluated with the classical benchmark functions and ...
"The beauty of tomosynthesis is that it addresses two major concerns with screening mammography: missed cancers and false positive rates," lead researcher Elizabeth A. Rafferty, MD, said in a press release. Rafferty is the director of... MV Durning - 《Diagnostic Imaging》 被引量: 0发表: 20...
CADx: Computer-aided diagnosis CC: Craniocaudal DBT: Digital breast tomosynthesis DCNN: Deep convolutional neural network DTL: Double transfer learning FFDM: Full-field digital mammography MIX: Mixture of DBT&FFDM ML: Mediolateral MLO: Mediolateral oblique PACS: Picture archiving and commu...
We used diagnostic support software (Lunit insight MMG, Lunit, Seoul, Korea, available at https://insight.lunit.io, accessed 10 July 2021). Given the bilateral routine four-view protocol, AI-CAD provided a malignancy score of suspicious microcalcification in terms of a percentage (Figure 2 and...
The average computation time for feature extraction is five seconds per bilateral mammogram image. Type 1 and Type 2 fuzzy classifiers have one millisecond execution time per case. The overall CADx computation time is calculated at around five seconds. The system is designed to run as a cloud ...
The average computation time for feature extraction is five seconds per bilateral mammogram image. Type 1 and Type 2 fuzzy classifiers have one millisecond execution time per case. The overall CADx computation time is calculated at around five seconds. The system is designed to run as a cloud ...
neural network trained on over 210,000 screening mammograms with 5379 cancer cases (cancer incidence, 2.6%), whereas, in this study, we used a cancer-enriched dataset for the purpose of effective training based on distinctive mammographic parenchymal patterns from both screening and diagnostic ...
neural network trained on over 210,000 screening mammograms with 5379 cancer cases (cancer incidence, 2.6%), whereas, in this study, we used a cancer-enriched dataset for the purpose of effective training based on distinctive mammographic parenchymal patterns from both screening and diagnostic ...