We also investigate effect of ensemble learning using multiple sequences, which could provide references to choose appropriate sequences for PLDC. In each DA setting, the models using multiple sequences are always more effective than using any single sequence alone. Besides, although ADC or hDWI always leads to the worst classification results, T2 ensembled with one/both of them can explicitly enhance the model’s performance. This finding is consistent with the clinical practice of using mpMRI for PCa diagnosis. Sequences ADC and hDWI are usually considered as secondary references by radiologists. It should be noted that the all-sequence-ensembled (i.e. ensemble of T2, ADC, and hDWI) models show significant predictions in most DA settings. Although ensemble of the three sequences could not lead to the best performance in the second DA setting (i.e. P-x → LC-A), the model of the second DA setting still attains a remarkable AUC of 0.91, which is only about 1% smaller than the highest AUC (0.92). It can be concluded that using more sequences would help multi-cohort MRI harmonization, thus boosting the final classification performance. Moreover, with the same target domain (i.e. either LC-A or LC-B), the CMD²A-Net transferred from P-x attains a higher AUC than transferred from a local cohort domain in each sequence combination. This implies more source samples could enhance the model’s cross-domain knowledge transferability, thus improving the model’s generalization in the target domain. The superior performance also demonstrates CMD²A-Net’s capability of transferring the knowledge of a public dataset to our local cohort domains.