Model training
CERVICAL CANCER
The training processes of CTV and GTV DpnUnet networks were the same but independently with different training data. There were 121 patient cases in the training group. The DpnUnet networks were trained with 11-fold (10:1) cross-validation, 110 cases for training and the other 11 cases for validation. Generic data augmentation techniques including flip, and translation, rotation were used. The networks were implemented by PyTorch 1.12.0 and Python 3.6, and trained on a NVIDIA P100 GPU (16 GB memory). Both of the CTV and GTV kernel networks were initialized with a pre-trained network that had been trained for OAR segmentation in cervical cancer CT images [