Xeon r cpu e5 2680 v4

In the training stage, we applied a batch size of 40, a learning rate of 0.001, and 100 epochs to train our model. Considering the shape of our input images, we resized the patches to 1024 × 1024, reducing the computational resources. Moreover, we adopted the Adam solver [42 ] to optimize model parameters during the training phase. All the experiments were conducted in Pytorch [43 ] under an Ubuntu OS cloud server with an Intel Xeon(R) CPU E5-2680 v4 @2.40 GHz, 40 GB of RAM, and an NVIDIA Tesla P40 GPU with 24 GB of memory.

In the training stage, all the training sets were shuffled, and all input images were normalized to the range of 0–1, and the batch size was set to 64. We optimized the generator and the discriminator alternately, both applying the Adam solver with a fixed learning rate of 0.0002 and momentum parameters of β1 = 0.5 and β2 = 0.999. Then, we set the random seed to 123. We trained our framework from scratch with the training sets to produce the “optimized” model. The training was stopped when training losses did not decrease for 200 consecutive epochs. We saved the generator model weights when the training Dice scores were at their highest. For the inference stage, we used the well-trained framework to segment the images. All the experiments were conducted in Pytorch [48 (link)] under an Ubuntu OS cloud server with an Intel Xeon(R) CPU E5-2680 v4 @2.40 GHz, 40 GB of RAM, and an NVIDIA Tesla P40 GPU with 24 GB of memory.