where \({n_i}\) denotes the number of images in the corresponding domain, and
\({{\rm{D}}_i}\) indicates the feature matrices of the corresponding FC layer, and 1 is a column vector with all elements as 1.
To accomplish malignancy prediction using mpMRI, an ensemble learning
approach is employed to fuse the predictions of the three separated
models (w.r.t T2, ADC, and hDWI). We train the classifier module, as in Figure 4 , using labeled source data. The FC layers in the
source domain are employed, not only for cross-domain feature affinity,
but also for malignancy classification. The cross-entropy loss is
utilized to optimize the classifier module. Our classification loss \({\mathcal{L}_{Cls}}\) can
be defined as: