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[11-12] sequences may vary in healthcare institutions. Thus, we employed ensemble learning[13] 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).