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]