Titan 5 gpu

An appropriate parameter setting is crucial to the successful training of deep convolutional neural networks. We selected the number of epochs to stop the training by contrasting training loss and the performance on validation set over epochs in each experiment, as shown in Figure S2 in the Supplement. Hence, we choose a number of N epochs to avoid over‐fitting and to keep a low computational cost by observing the VS and DSC on the validation set. The batch size was empirically set to 30 and the learning rate was set to 0.0002 throughout all experiments by observing the training stability on the validation set.
The experiments are conducted on a GNU/Linux server running Ubuntu 18.04, with 64GB RAM. The number of trainable parameters in the proposed model with one‐channel inputs (T1) is 4,641,209. The algorithm was trained on a single NVIDIA Titan‐V GPU with 12GB RAM. It takes around 100 min to train a single model for 200 epochs on a training set containing 5000 images with a size of 180 × 180 pixels. For testing, the segmentation of one scan with 192 slices by an ensemble of two models takes around 90 s using an Intel Xeon CPU (E3‐1225v3) (without GPU use). In contrast, the segmentation per scan takes only 3 s when using a GPU.

RNAForecaster was trained as an ensemble of 10 networks, training for 20 epochs on all 405 cells with a 60-min labeling period. These networks were trained on a Nvidia Titan V GPU using a mini-batch size of 100 and a learning rate of 0.001. All other parameters use the default values.

To assess the overall performance of the model, 19 patients with 1104 CT slices were randomly selected as a test set, and a 5-fold cross-validation procedure was then performed on the remaining 72 patients with 3482 CT slices. Each of the 5 models divided the 3482 CT slices into 80% in the training set and 20% in the validation set. Five separate models were initialized, trained, and validated in a unique combination of training and validation. Each model predicted a pixel classification label from the CT image. From these 5 trained models, we took the best-performing model based on its validation and training DSC and evaluated this model in the test set.
The Adam algorithm was chosen as the optimizer to minimize the loss function. We used a learning rate of 1 × 10⁻⁴ and the default Adam parameters β₁ = 0.9, β₂ = 0.999, and decay = 0. Because of the fast convergence of the improved U-Net and DDUnet, the evolution stopped at approximately 40 epochs. Thus, we chose 40 epochs when training the model. The deep network architecture was implemented in Keras2.1.6 with TensorFlow1.5 as the backend. One NVIDIA TITAN V GPU with 12-GB memory was used for training and testing.
The model was trained on a single slice of the patient’s CT images. The output was pixel-level classification for that patient slice. The training batch size was 6 slices.