Domain adaptation model training for mpMRI-based PLDC

For DA model (i.e., CMD²A-Net) training, the prostate regions from both the source and target domains are scaled to 224 × 224 pixels. Random rotation is applied for data augmentation. Adam optimizer is chosen.
You need to first   In the training process of CM-Net, due to the limited sample size, all the slices were split into training and testing sets in the ratio of 4:1 using the hold-out method. The segmentation loss was optimized first to accelerate model convergence, and CM-Net with the pre-trained coarse segmentation module was further trained. In terms of CMD²A-Net, we initialized its both branches first using the weight of pre-trained CM-Net, in order to facilitate its convergence. To be specific, we trained both the coarse segmentation module and classifier of CM-Net first, with the combined samples from both domains. Then, we optimized the total loss of CMD²A-Net with labeled source samples and unlabeled target samples. By co-training all the modules, the model with the highest accuracy was saved for malignancy evaluation in the target domain.