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.