Review Article
Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging
Table 1
Brief overview of common data-driven techniques used in breast MRI.
| Technique | Advantages | Disadvantages | References |
| Supervised learning | | | | Ensemble of decision trees | Decision using branches Variable significance and feature selection are included | Prone to overfitting | [12–14] | [15, 16] | Random forest | High performance Compared to decision trees | Prone to overfitting | [14, 17, 18] | [19] | Support vector machines | Transforms nonlinear classification problem into linear one High accuracy | Difficult computation in high-dimensional data space | [20, 21] | [22, 23] | [24] | Neural networks | Weights need to be adapted for training Multiclass classification | No strategy to determine network structure | [25–27] | [28, 29] | [30, 31] | Deep learning | State-of-the-art in image-derived features | Computationally intensive Hard to interpret | [32, 33] | [34–36] | [37–39] |
| Unsupervised learning | | | | Clustering (k-means) | Brief training duration | Number of clusters must be known in advance | [40, 41] | Topological data analysis | Interpretable data mapping Discovery of variable relationships | Divided clusters due to mapping | [28, 42, 43] |
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