*2.5. The Transfer-Training Process*

The aim of the transfer-training process is to fine-tune the NI-U-Net++ from the "Pre-trained weights" to the "Final weights" for the real-life images (see Figure 1). The "Annotated visual dataset" is divided into training, validation, and testing sets according to the ratio of 80%:10%:10% (similar to the pre-training process). The hyperparameters have been depicted in Table 4: the number of epochs is set to 50 epochs, the batch size is set to 5 samples per batch, the learning rate is set to 0.00005, the optimizer uses the Adam, and the loss function uses the binary cross-entropy. The evaluation also uses the three popular metrics, accuracy, IoU, and Dice score.


**Table 4.** The hyperparameters of the transfer-training process.

The data for the transfer-training process comes from "Annotation-2" in Figure 1. "Annotation-2" can be composed of the following four steps.


changes in imaging conditions. The data augmentation eventually achieves about 4000 images.
