Research Article
Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging
Table 1
Performance parameters on training and validation data.
| | Training | Validation DSC | Hinge loss | Accuracy | Specificity | Sensitivity | | Max. | DSC() [DSC()] |
| PCA + linear SVM | 0.9764 | 0.7263 | 0.6581 | 0.7944 | 0.310.01 | 0.33820.0005 | 0.3310 [0.3039 − 0.2169] | PCA + RBFSVM | 0.9529 | 0.7263 | 0.6581 | 0.7944 | 0.310.01 | 0.33820.0005 | 0.3310 [0.3039 − 0.2169] | ICA + linear SVM | 0.1254 | 0.9501 | 0.9410 | 0.9593 | 0.310.01 | 0.530.01 | 0.5295 [0.475 − 0.484] | ICA + RBFSVM | 0.1083 | 0.9515 | 0.9573 | 0.9457 | 0.290.01 | 0.440.04 | 0.1085 [0.3711 − 0.0559] | Raw + linear SVM | 2.4429 | 0.8026 | 0.8446 | 0.7605 | 0.150.07 | 0.300.05 | 0.2325 [0.1373 − 0.3058] |
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