Review Article
Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging
Table 3
Detection of breast lesions in breast MRI using DL.
| DL technique | Evaluation results | Dataset | References |
| Model agnostic saliency | TPR = 80 FPs/image = 8 | 117 subjects DCE-MRI and T1W images | [86] | U-net | Acc = 94.2 | 67 MR images T1W, T2W, DWI, and DCE-MRI | [87] | Patch-based analysis with ResNet50 backbone | AUC = 0.817 | 335 MR images of 17 different histological subtypes | [65] | Deep Q-network | Sn = 80 FPs/image = 3.2 | 117 DCE-MR and T1-weighted images | [63] | Unsupervised saliency analysis and CNN | Acc = 86 ± 2 AUC = 0.94 ± 0.01 | 193 DCE-MR images | [88] | Two-level U-net and dual-stream CNN | CPM = 64.29 | Training: 201 DCE-MR images Testing: 160 DCE-MR images | [64] |
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