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

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2023-11-07 DOI:10.1109/TRPMS.2023.3330772
Lu Wen;Jianghong Xiao;Chen Zu;Xi Wu;Jiliu Zhou;Xingchen Peng;Yan Wang
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引用次数: 0

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.
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剂量转移:用于宫颈癌放疗剂量预测的具有迁移学习功能的变压器嵌入式模型
宫颈癌是突出的女性恶性肿瘤,严重威胁着妇女的健康。临床解决方案通常包括费时费力的放疗计划。虽然已经研究了基于卷积神经网络(CNN)的模型,通过预测放疗结果(即剂量分布图)来实现放疗计划的自动化,但宫颈癌数据集的数据不足限制了模型的预测性能和泛化。此外,卷积操作的固有局部性也阻碍了模型捕捉全局范围内的剂量信息,从而限制了预测的准确性。在这篇文章中,我们提出了一种嵌入了转换器的迁移学习框架,即 DoseTransfer,用于自动预测宫颈癌的剂量分布。针对宫颈癌数据集数据有限的问题,我们利用了直肠癌中高度相关的临床信息,并在一个两阶段框架中转移了这些知识。具体来说,第一阶段是预训练阶段,旨在利用直肠癌数据集对模型进行预训练,并从直肠癌中提取先验知识;第二阶段是转移阶段,将先验知识有效地转移到宫颈癌中,并指导模型达到更高的准确度。此外,这两个阶段都嵌入了转换器,以捕捉 CNN 忽略的全局依赖关系,学习更广泛的特征表征。在内部数据集(即直肠癌数据集和宫颈癌数据集)上的实验结果证明了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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Affiliate Plan of the IEEE Nuclear and Plasma Sciences Society Table of Contents Introducing IEEE Collabratec IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information Member Get-a-Member (MGM) Program
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