Table 3.

Different feature datasets and corresponding classifier strategies considered in this work for multi-parametric data analysis.

Description		Data vectors	Classifier
Single Protocol	T2w	${\mathbf{F}}^{T2}(c)=[f^{T2}(c),f_{\varphi }^{T2}(c)|\varphi \in \{1,\dots ,5\}]$	hT2(c)
	DCE	FT1(c) = [fT1,t(c)|t ∈ {1, …, 6}]	hT1(c)
	ADC	${\mathbf{F}}^{ADC}(c)=[f^{ADC}(c),f_{\varphi }^{ADC}(c)|\varphi \in \{1,\dots ,5\}]$	hADC(c)
Multi-parametric	Features	FFeat(c) = [FT2(c),FT1(c),FADC]	hFeat(c)
	EMPrAvISE	FEm(c) = [ẽv(c)|v ∈ {1, …, n}]	|hEm(c), ${\mathbf{h}}_{MRF}^{Em}$