Cross-domain Malignancy Classification and Lesion Detection

We emphasize the importance of knowledge transfer from a large-scale publicly dataset to a small-scale target domain. The malignancy estimation performance of CMD²A-Net (the architecture is shown in Figure 4 and described in detail in the Methods section) is evaluated. Dataset, P-x, is only regarded as the source domain. Either LC-A or LC-B is also set as the source domain for knowledge transfer between local cohorts. The scaled method was employed for image preprocessing. In general, available types of MR sequences may vary in healthcare institutions. Thus, we employed ensemble learning[37] to handle multiple sequences, allowing the use of single and multiple sequence(s) in our framework. Three common metrics were adopted for classification performance evaluation, i.e. AUC, sensitivity (SEN), and specificity (SPE).
Table 4. Malignancy classification results in the target domains in four combinations of source-target domain.