Polymerized Feature-based Domain Adaptation for Cervical Cancer Dose Map
  Prediction
Jie Zeng Zeyu Han Xingchen Peng Jianghong Xiao Peng Wang Yan Wang
Recently, deep learning (DL) has automated and accelerated the clinical
radiation therapy (RT) planning significantly by predicting accurate dose maps.
However, most DL-based dose map prediction methods are data-driven and not
applicable for cervical cancer where only a small amount of data is available.
To address this problem, this paper proposes to transfer the rich knowledge
learned from another cancer, i.e., rectum cancer, which has the same scanning
area and more clinically available data, to improve the dose map prediction
performance for cervical cancer through domain adaptation. In order to close
the congenital domain gap between the source (i.e., rectum cancer) and the
target (i.e., cervical cancer) domains, we develop an effective
Transformer-based polymerized feature module (PFM), which can generate an
optimal polymerized feature distribution to smoothly align the two input
distributions. Experimental results on two in-house clinical datasets
demonstrate the superiority of the proposed method compared with
state-of-the-art methods.