DoseTransfer: A Transformer Embedded Model With Transfer Learning for Radiotherapy Dose Prediction of Cervical Cancer

Abstract

Cervical cancer stands as a prominent female malignancy, posing a serious threat to women’s health. The clinical solution typically involves time-consuming and laborious radiotherapy planning. Although convolutional neural network (CNN)-based models have been investigated to automate the radiotherapy planning by predicting its outcomes, i.e., dose distribution maps, the insufficiency of data in the cervical cancer dataset limits the prediction performance and generalization of models. Additionally, the intrinsic locality of convolution operations also hinders models from capturing dose information at a global range, limiting the prediction accuracy. In this article, we propose a transfer learning framework embedded with transformer, namely, DoseTransfer, to automatically predict the dose distribution for cervical cancer. To address the limited data in the cervical cancer dataset, we leverage highly correlated clinical information from rectum cancer and transfer this knowledge in a two-phase framework. Specifically, the first phase is the pretraining phase which aims to pretrain the model with the rectum cancer dataset and extract prior knowledge from rectum cancer, while the second phase is the transferring phase where the priorly learned knowledge is effectively transferred to cervical cancer and guides the model to achieve better accuracy. Moreover, both phases are embedded with transformers to capture the global dependencies ignored by CNN, learning wider feature representations. Experimental results on the in-house datasets (i.e., rectum cancer dataset and cervical cancer dataset) have demonstrated the effectiveness of the proposed method.